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Sutoh Y, Hachiya T, Otsuka-Yamasaki Y, Komaki S, Minabe S, Ohmomo H, Sasaki M, Shimizu A. Healthy lifestyle practice correlates with decreased obesity prevalence in individuals with high polygenic risk: TMM CommCohort study. J Hum Genet 2024:10.1038/s10038-024-01280-3. [PMID: 39174808 DOI: 10.1038/s10038-024-01280-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 06/24/2024] [Accepted: 07/24/2024] [Indexed: 08/24/2024]
Abstract
Obesity and overweight, fundamental components of the metabolic syndrome, predispose individuals to lifestyle-related diseases. The extent to which adopting healthy lifestyles can reduce obesity risk, even in those with a high genetic risk, remains uncertain. Our aim was to assess the extent to which lifestyle modifications can improve outcomes in individuals with a high polygenic score (PGS) for obesity. We quantified the genetic risk of obesity using PGSs. Four datasets from the Tohoku Medical Megabank Community-Based Cohort (TMM CommCohort) were employed in the study. One dataset (n = 9958) was used to select the best model for calculating PGS. The remaining datasets (total n = 69,341) were used in a meta-analysis to validate the model and to evaluate associated risks. The odds ratio (OR) for obesity risk in the intermediate (11th-90th percentiles in the dataset) and high PGS categories (91st-100th) was 2.27 [95% confidence intervals: 2.12-2.44] and 4.83 [4.45-5.25], respectively, compared to that in the low PGS category (1st-10th). Trend analysis showed that an increase in leisure-time physical activity was significantly associated with reduced obesity risk across all genetic risk categories, representing an OR of 0.9 [0.87-0.94] even among individuals in the high PGS category. Similarly, sodium intake displayed a positive association with obesity across all genetic risk categories, yielding an OR of 1.24 [1.17-1.31] in the high PGS category. The risk of obesity was linked to the adoption of healthy lifestyles, even in individuals with high PGS. Our results may provide perspectives for integrating PGSs into preventive medicine.
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Affiliation(s)
- Yoichi Sutoh
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Disaster Reconstruction Center, Iwate Medical University, Yahaba, Japan
- Division of Biomedical Information Analysis, Institute for Biomedical Sciences, Iwate Medical University, Yahaba, Japan
| | - Tsuyoshi Hachiya
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Disaster Reconstruction Center, Iwate Medical University, Yahaba, Japan
- Division of Biomedical Information Analysis, Institute for Biomedical Sciences, Iwate Medical University, Yahaba, Japan
| | - Yayoi Otsuka-Yamasaki
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Disaster Reconstruction Center, Iwate Medical University, Yahaba, Japan
- Division of Biomedical Information Analysis, Institute for Biomedical Sciences, Iwate Medical University, Yahaba, Japan
| | - Shohei Komaki
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Disaster Reconstruction Center, Iwate Medical University, Yahaba, Japan
- Division of Biomedical Information Analysis, Institute for Biomedical Sciences, Iwate Medical University, Yahaba, Japan
| | - Shiori Minabe
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Disaster Reconstruction Center, Iwate Medical University, Yahaba, Japan
- Division of Biomedical Information Analysis, Institute for Biomedical Sciences, Iwate Medical University, Yahaba, Japan
| | - Hideki Ohmomo
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Disaster Reconstruction Center, Iwate Medical University, Yahaba, Japan
- Division of Biomedical Information Analysis, Institute for Biomedical Sciences, Iwate Medical University, Yahaba, Japan
| | - Makoto Sasaki
- Iwate Tohoku Medical Megabank Organization, Disaster Reconstruction Center, Iwate Medical University, Yahaba, Japan
- Division of Ultrahigh Field MRI, Institute for Biomedical Sciences, Iwate Medical University, Yahaba, Japan
| | - Atsushi Shimizu
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Disaster Reconstruction Center, Iwate Medical University, Yahaba, Japan.
- Division of Biomedical Information Analysis, Institute for Biomedical Sciences, Iwate Medical University, Yahaba, Japan.
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2
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Serio VB, Rosati D, Maffeo D, Rina A, Ghisalberti M, Bellan C, Spiga O, Mari F, Palmieri M, Frullanti E. The Personalized Inherited Signature Predisposing to Non-Small-Cell Lung Cancer in Non-Smokers. Cancers (Basel) 2024; 16:2887. [PMID: 39199663 PMCID: PMC11352340 DOI: 10.3390/cancers16162887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2024] [Revised: 08/14/2024] [Accepted: 08/19/2024] [Indexed: 09/01/2024] Open
Abstract
Lung cancer (LC) continues to be an important public health problem, being the most common form of cancer and a major cause of cancer deaths worldwide. Despite the great bulk of research to identify genetic susceptibility genes by genome-wide association studies, only few loci associated to nicotine dependence have been consistently replicated. Our previously published study in few phenotypically discordant sib-pairs identified a combination of germline truncating mutations in known cancer susceptibility genes in never-smoker early-onset LC patients, which does not present in their healthy sib. These results firstly demonstrated the presence of an oligogenic combination of disrupted cancer-predisposing genes in non-smokers patients, giving experimental support to a model of a "private genetic epidemiology". Here, we used a combination of whole-exome and RNA sequencing coupled with a discordant sib's model in a novel cohort of pairs of never-smokers early-onset LC patients and in their healthy sibs used as controls. We selected rare germline variants predicted as deleterious by CADD and SVM bioinformatics tools and absent in the healthy sib. Overall, we identified an average of 200 variants per patient, about 10 of which in cancer-predisposing genes. In most of them, RNA sequencing data reinforced the pathogenic role of the identified variants showing: (i) downregulation in LC tissue (indicating a "second hit" in tumor suppressor genes); (ii) upregulation in cancer tissue (likely oncogene); and (iii) downregulation in both normal and cancer tissue (indicating transcript instability). The combination of the two techniques demonstrates that each patient has an average of six (with a range from four to eight) private mutations with a functional effect in tumor-predisposing genes. The presence of a unique combination of disrupting events in the affected subjects may explain the absence of the familial clustering of non-small-cell lung cancer. In conclusion, these findings indicate that each patient has his/her own "predisposing signature" to cancer development and suggest the use of personalized therapeutic strategies in lung cancer.
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Affiliation(s)
- Viola Bianca Serio
- Cancer Genomics & Systems Biology Laboratory, University of Siena, 53100 Siena, Italy; (V.B.S.); (D.R.); (D.M.); (M.P.)
- Med Biotech Hub and Competence Centre, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (A.R.); (F.M.)
| | - Diletta Rosati
- Cancer Genomics & Systems Biology Laboratory, University of Siena, 53100 Siena, Italy; (V.B.S.); (D.R.); (D.M.); (M.P.)
- Med Biotech Hub and Competence Centre, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (A.R.); (F.M.)
| | - Debora Maffeo
- Cancer Genomics & Systems Biology Laboratory, University of Siena, 53100 Siena, Italy; (V.B.S.); (D.R.); (D.M.); (M.P.)
- Med Biotech Hub and Competence Centre, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (A.R.); (F.M.)
| | - Angela Rina
- Med Biotech Hub and Competence Centre, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (A.R.); (F.M.)
| | - Marco Ghisalberti
- Thoracic Surgery Unit, Azienda Ospedaliera Universitaria Senese, 53100 Siena, Italy;
| | - Cristiana Bellan
- Department of Medical Biotechnology, Section of Pathology, University of Siena, 53100 Siena, Italy;
| | - Ottavia Spiga
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy;
| | - Francesca Mari
- Med Biotech Hub and Competence Centre, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (A.R.); (F.M.)
- UOC Laboratorio di Assistenza e Ricerca Traslazionale, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy
| | - Maria Palmieri
- Cancer Genomics & Systems Biology Laboratory, University of Siena, 53100 Siena, Italy; (V.B.S.); (D.R.); (D.M.); (M.P.)
- Med Biotech Hub and Competence Centre, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (A.R.); (F.M.)
| | - Elisa Frullanti
- Cancer Genomics & Systems Biology Laboratory, University of Siena, 53100 Siena, Italy; (V.B.S.); (D.R.); (D.M.); (M.P.)
- Med Biotech Hub and Competence Centre, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy; (A.R.); (F.M.)
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Nguyen OTD, Fotopoulos I, Nøst TH, Markaki M, Lagani V, Tsamardinos I, Røe OD. The HUNT lung-SNP model: genetic variants plus clinical variables improve lung cancer risk assessment over clinical models. J Cancer Res Clin Oncol 2024; 150:389. [PMID: 39129029 PMCID: PMC11317451 DOI: 10.1007/s00432-024-05909-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Accepted: 07/29/2024] [Indexed: 08/13/2024]
Abstract
PURPOSE The HUNT Lung Cancer Model (HUNT LCM) predicts individualized 6-year lung cancer (LC) risk among individuals who ever smoked cigarettes with high precision based on eight clinical variables. Can the performance be improved by adding genetic information? METHODS A polygenic model was developed in the prospective Norwegian HUNT2 study with clinical and genotype data of individuals who ever smoked cigarettes (n = 30749, median follow up 15.26 years) where 160 LC were diagnosed within six years. It included the variables of the original HUNT LCM plus 22 single nucleotide polymorphisms (SNPs) highly associated with LC. External validation was performed in the prospective Norwegian Tromsø Study (n = 2663). RESULTS The novel HUNT Lung-SNP model significantly improved risk ranking of individuals over the HUNT LCM in both HUNT2 (p < 0.001) and Tromsø (p < 0.05) cohorts. Furthermore, detection rate (number of participants selected to detect one LC case) was significantly better for the HUNT Lung-SNP vs. HUNT LCM in both cohorts (42 vs. 48, p = 0.003 and 11 vs. 14, p = 0.025, respectively) as well as versus the NLST, NELSON and 2021 USPSTF criteria. The area under the receiver operating characteristic curve (AUC) was higher for the HUNT Lung-SNP in both cohorts, but significant only in HUNT2 (AUC 0.875 vs. 0.844, p < 0.001). However, the integrated discrimination improvement index (IDI) indicates a significant improvement of LC risk stratification by the HUNT Lung-SNP in both cohorts (IDI 0.019, p < 0.001 (HUNT2) and 0.013, p < 0.001 (Tromsø)). CONCLUSION The HUNT Lung-SNP model could have a clinical impact on LC screening and has the potential to replace the HUNT LCM as well as the NLST, NELSON and 2021 USPSTF criteria in a screening setting. However, the model should be further validated in other populations and evaluated in a prospective trial setting.
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Affiliation(s)
- Olav Toai Duc Nguyen
- Department of Clinical Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Prinsesse Kristinas gate. 1, Trondheim, NO, 7030, Norway
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Cancer Clinic, Kirkegata 2, Levanger, NO, 7600, Norway
| | - Ioannis Fotopoulos
- Department of Computer Science, University of Crete, Voutes Campus, Heraklion, GR, 70013, Greece
| | - Therese Haugdahl Nøst
- Department of Community Medicine, Faculty of Health Sciences, UiT The Arctic University of Norway, P.O. Box 6050, Langnes, Tromsø, NO-9037, Norway
- Department of Public Health and Nursing, Norwegian University of Science and Technology, K.G. Jebsen Center for Genetic Epidemiology, NTNU, Håkon Jarls Gate 12, Trondheim, 7030, Norway
| | - Maria Markaki
- Institute of Applied and Computational Mathematics, FORTH, Heraklion, Crete, GR-700 13, Greece
| | - Vincenzo Lagani
- Biological and Environmental Sciences and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23952, Saudi Arabia
- SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence, Thuwal, 23952, Saudi Arabia
- Institute of Chemical Biology, Ilia State University, Tbilisi, 0162, Georgia
| | - Ioannis Tsamardinos
- Department of Computer Science, University of Crete, Voutes Campus, Heraklion, GR, 70013, Greece
- Institute of Applied and Computational Mathematics, FORTH, Heraklion, Crete, GR-700 13, Greece
- JADBio Gnosis DA S.A, STEP-C, N. Plastira 100, Heraklion, 700-13, GR, Greece
| | - Oluf Dimitri Røe
- Department of Clinical Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), Prinsesse Kristinas gate. 1, Trondheim, NO, 7030, Norway.
- Levanger Hospital, Nord-Trøndelag Hospital Trust, Cancer Clinic, Kirkegata 2, Levanger, NO, 7600, Norway.
- Clinical Cancer Research Center, Department of Clinical Medicine, Aalborg University Hospital, Hobrovej 18-22, Aalborg, DK-9100, Denmark.
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Li S, Chen J, Zhou B. The clinical significance of endoplasmic reticulum stress related genes in non-small cell lung cancer and analysis of single nucleotide polymorphism for CAV1. Front Mol Biosci 2024; 11:1414164. [PMID: 39165641 PMCID: PMC11334084 DOI: 10.3389/fmolb.2024.1414164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 07/09/2024] [Indexed: 08/22/2024] Open
Abstract
In recent years, protein homeostasis imbalance caused by endoplasmic reticulum stress has become a major hallmark of cancer. Studies have shown that endoplasmic reticulum stress is closely related to the occurrence, development, and drug resistance of non-small cell lung cancer, however, the role of various endoplasmic reticulum stress-related genes in non-small cell lung cancer is still unclear. In this study, we established an endoplasmic reticulum stress scores based on the Cancer Genome Atlas for non-small cell lung cancer to reflect patient features and predict prognosis. Survival analysis showed significant differences in overall survival among non-small cell lung cancer patients with different endoplasmic reticulum stress scores. In addition, endoplasmic reticulum stress scores was significantly correlated with the clinical features of non-small cell lung cancer patients, and can be served as an independent prognostic indicator. A nomogram based on endoplasmic reticulum stress scores indicated a certain clinical net benefit, while ssGSEA analysis demonstrated that there was a certain immunosuppressive microenvironment in high endoplasmic reticulum stress scores. Gene Set Enrichment Analysis showed that scores was associated with cancer pathways and metabolism. Finally, weighted gene co-expression network analysis displayed that CAV1 was closely related to the occurrence of non-small cell lung cancer. Therefore, in order to further analyze the role of this gene, Chinese non-smoking females were selected as the research subjects to investigate the relationship between CAV1 rs3779514 and susceptibility and prognosis of non-small cell lung cancer. The results showed that the mutation of rs3779514 significantly reduced the risk of non-small cell lung cancer in Chinese non-smoking females, but no prognostic effect was found. In summary, we proposed an endoplasmic reticulum stress scores, which was an independent prognostic factor and indicated immune characteristics in the microenvironment of non-small cell lung cancer. We also validated the relationship between single nucleotide polymorphism locus of core genes and susceptibility to non-small cell lung cancer.
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Affiliation(s)
| | | | - Baosen Zhou
- Department of Clinical Epidemiology and Center of Evidence-Based Medicine, The First Hospital of China Medical University, Shenyang, China
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5
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Boumtje V, Manikpurage HD, Li Z, Gaudreault N, Armero VS, Boudreau DK, Renaut S, Henry C, Racine C, Eslami A, Bougeard S, Vigneau E, Morissette M, Arsenault BJ, Labbé C, Laliberté AS, Martel S, Maltais F, Couture C, Desmeules P, Mathieu P, Thériault S, Joubert P, Bossé Y. Polygenic inheritance and its interplay with smoking history in predicting lung cancer diagnosis: a French-Canadian case-control cohort. EBioMedicine 2024; 106:105234. [PMID: 38970920 PMCID: PMC11282926 DOI: 10.1016/j.ebiom.2024.105234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 06/19/2024] [Accepted: 06/25/2024] [Indexed: 07/08/2024] Open
Abstract
BACKGROUND The most near-term clinical application of genome-wide association studies in lung cancer is a polygenic risk score (PRS). METHODS A case-control dataset was generated consisting of 4002 lung cancer cases from the LORD project and 20,010 ethnically matched controls from CARTaGENE. A genome-wide PRS including >1.1 million genetic variants was derived and validated in UK Biobank (n = 5419 lung cancer cases). The predictive ability and diagnostic discrimination performance of the PRS was tested in LORD/CARTaGENE and benchmarked against previous PRSs from the literature. Stratified analyses were performed by smoking status and genetic risk groups defined as low (<20th percentile), intermediate (20-80th percentile) and high (>80th percentile) PRS. FINDINGS The phenotypic variance explained and the effect size of the genome-wide PRS numerically outperformed previous PRSs. Individuals with high genetic risk had a 2-fold odds of lung cancer compared to low genetic risk. The PRS was an independent predictor of lung cancer beyond conventional clinical risk factors, but its diagnostic discrimination performance was incremental in an integrated risk model. Smoking increased the odds of lung cancer by 7.7-fold in low genetic risk and by 11.3-fold in high genetic risk. Smoking with high genetic risk was associated with a 17-fold increase in the odds of lung cancer compared to individuals who never smoked and with low genetic risk. INTERPRETATION Individuals at low genetic risk are not protected against the smoking-related risk of lung cancer. The joint multiplicative effect of PRS and smoking increases the odds of lung cancer by nearly 20-fold. FUNDING This work was supported by the CQDM and the IUCPQ Foundation owing to a generous donation from Mr. Normand Lord.
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Affiliation(s)
- Véronique Boumtje
- Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Quebec City, Canada
| | - Hasanga D Manikpurage
- Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Quebec City, Canada
| | - Zhonglin Li
- Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Quebec City, Canada
| | - Nathalie Gaudreault
- Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Quebec City, Canada
| | - Victoria Saavedra Armero
- Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Quebec City, Canada
| | - Dominique K Boudreau
- Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Quebec City, Canada
| | - Sébastien Renaut
- Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Quebec City, Canada
| | - Cyndi Henry
- Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Quebec City, Canada
| | - Christine Racine
- Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Quebec City, Canada
| | - Aida Eslami
- Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Quebec City, Canada
| | - Stéphanie Bougeard
- Anses (French Agency for Food, Environmental and Occupational Health and Safety), 22440, Ploufragan, France
| | | | - Mathieu Morissette
- Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Quebec City, Canada
| | - Benoit J Arsenault
- Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Quebec City, Canada
| | - Catherine Labbé
- Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Quebec City, Canada
| | - Anne-Sophie Laliberté
- Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Quebec City, Canada
| | - Simon Martel
- Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Quebec City, Canada
| | - François Maltais
- Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Quebec City, Canada
| | - Christian Couture
- Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Quebec City, Canada
| | - Patrice Desmeules
- Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Quebec City, Canada
| | - Patrick Mathieu
- Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Quebec City, Canada
| | - Sébastien Thériault
- Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Quebec City, Canada
| | - Philippe Joubert
- Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Quebec City, Canada
| | - Yohan Bossé
- Institut universitaire de cardiologie et de pneumologie de Québec - Université Laval, Quebec City, Canada; Department of Molecular Medicine, Université Laval, Quebec City, Canada.
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Wang G, Zhu Z, Wang Y, Zhang Q, Sun Y, Pang G, Ge W, Ma Z, Ma H, Gong L, Ma H, Shao F, Zhu M. The association between METS-IR, an indirect index for insulin resistance, and lung cancer risk. Eur J Public Health 2024; 34:800-805. [PMID: 38300233 PMCID: PMC11293818 DOI: 10.1093/eurpub/ckad234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND Insulin resistance has been reported to increase the risk of breast, prostate and colorectal cancer. However, the role of insulin resistance and its interaction with genetic risk in the development of lung cancer remains controversial. Therefore, we aimed to explore the association between a novel metabolic score for insulin resistance (METS-IR) and lung cancer risk. METHODS A total of 395 304 participants without previous cancer at baseline were included. The Cox proportional hazards regression model was performed to investigate the association between METS-IR and lung cancer risk. In addition, a Mendelian randomization analysis was also performed to explore the causal relationship. The joint effects and additive interactions between METS-IR and polygenetic risk score (PRS) of lung cancer were also investigated. RESULTS During a median follow-up of 11.03 years (Inter-quartile range (IQR): 10.30-11.73), a total of 3161 incident lung cancer cases were diagnosed in 395 304 participants. There was a significant association between METS-IR and lung cancer risk, with an HR of 1.28 (95% CI: 1.17-1.41). Based on the Mendelian randomization analysis, however, no causal associations were observed. We observed a joint effect but no interaction between METS-IR and genetic risk. The lung cancer incidence was estimated to be 100.42 (95% CI: 91.45-109.38) per 100 000 person-year for participants with a high METS-IR and PRS, while only 42.76 (95% CI: 36.94-48.59) with low METS-IR and PRS. CONCLUSIONS High METS-IR was significantly associated with an increased risk of lung cancer. Keeping a low level of METS-IR might help reduce the long-term incident risk of lung cancer.
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Affiliation(s)
- Guoqing Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Zhaopeng Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yi Wang
- Department of Respiratory Disease, Nanjing Chest Hospital, Nanjing Medical University, Nanjing, China
| | - Qiang Zhang
- Department of Thoracic Surgery, Nanjing Chest Hospital, Nanjing Medical University, Nanjing, China
| | - Yungang Sun
- Department of Thoracic Surgery, Nanjing Chest Hospital, Nanjing Medical University, Nanjing, China
| | - Guanlian Pang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Wenjing Ge
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Zhimin Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Huimin Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Linnan Gong
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Feng Shao
- Department of Thoracic Surgery, Nanjing Chest Hospital, Nanjing Medical University, Nanjing, China
| | - Meng Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Department of Epidemiology, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
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7
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Xu H, Wu Y, Chen Q, Yu Y, Meng Q, Qin N, Zhang W, Tao X, Li S, Tian T, Zhang L, Ma H, Cui J, Chu M. Integrating apaQTL and eQTL analysis identifies a potential causal variant associated with lung adenocarcinoma risk in the Chinese population. Commun Biol 2024; 7:860. [PMID: 39003419 PMCID: PMC11246497 DOI: 10.1038/s42003-024-06502-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Accepted: 06/25/2024] [Indexed: 07/15/2024] Open
Abstract
Alternative polyadenylation (APA) plays a crucial role in cancer biology. Here, we used data from the 3'aQTL-atlas, GTEx, and the China Nanjing Lung Cancer GWAS database to explore the association between apaQTL/eQTL-SNPs and the risk of lung adenocarcinoma (LUAD). The variant T allele of rs277646 in NIT2 is associated with an increased risk of LUAD (OR = 1.12, P = 0.015), lower PDUI values, and higher NIT2 expression. The 3'RACE experiment showed multiple poly (A) sites in NIT2, with the rs277646-T allele causing preferential use of the proximal poly (A) site, resulting in a shorter 3'UTR transcript. This leads to the loss of the hsa-miR-650 binding site, thereby affecting LUAD malignant phenotypes by regulating the expression level of NIT2. Our findings may provide new insights into understanding and exploring APA events in LUAD carcinogenesis.
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Affiliation(s)
- Huiwen Xu
- Department of Epidemiology, School of Public Health, Nantong University, Nantong, Jiangsu, China
| | - Yutong Wu
- Department of Epidemiology, School of Public Health, Nantong University, Nantong, Jiangsu, China
| | - Qiong Chen
- Department of Epidemiology, School of Public Health, Nantong University, Nantong, Jiangsu, China
| | - Yuhui Yu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Qianyao Meng
- Department of Global Health and Population, School of Public Health, Harvard University, Boston, MA, USA
| | - Na Qin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Wendi Zhang
- Department of Epidemiology, School of Public Health, Nantong University, Nantong, Jiangsu, China
| | - Xiaobo Tao
- Department of Epidemiology, School of Public Health, Nantong University, Nantong, Jiangsu, China
| | - Siqi Li
- Department of Epidemiology, School of Public Health, Nantong University, Nantong, Jiangsu, China
| | - Tian Tian
- Department of Epidemiology, School of Public Health, Nantong University, Nantong, Jiangsu, China
| | - Lei Zhang
- Department of Epidemiology, School of Public Health, Nantong University, Nantong, Jiangsu, China
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Jiahua Cui
- Department of Epidemiology, School of Public Health, Nantong University, Nantong, Jiangsu, China.
| | - Minjie Chu
- Department of Epidemiology, School of Public Health, Nantong University, Nantong, Jiangsu, China.
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Long E, Patel H, Golden A, Antony M, Yin J, Funderburk K, Feng J, Song L, Hoskins JW, Amundadottir LT, Hung RJ, Amos CI, Shi J, Rothman N, Lan Q, Choi J. High-throughput characterization of functional variants highlights heterogeneity and polygenicity underlying lung cancer susceptibility. Am J Hum Genet 2024; 111:1405-1419. [PMID: 38906146 PMCID: PMC11267514 DOI: 10.1016/j.ajhg.2024.05.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 05/23/2024] [Accepted: 05/23/2024] [Indexed: 06/23/2024] Open
Abstract
Genome-wide association studies (GWASs) have identified numerous lung cancer risk-associated loci. However, decoding molecular mechanisms of these associations is challenging since most of these genetic variants are non-protein-coding with unknown function. Here, we implemented massively parallel reporter assays (MPRAs) to simultaneously measure the allelic transcriptional activity of risk-associated variants. We tested 2,245 variants at 42 loci from 3 recent GWASs in East Asian and European populations in the context of two major lung cancer histological types and exposure to benzo(a)pyrene. This MPRA approach identified one or more variants (median 11 variants) with significant effects on transcriptional activity at 88% of GWAS loci. Multimodal integration of lung-specific epigenomic data demonstrated that 63% of the loci harbored multiple potentially functional variants in linkage disequilibrium. While 22% of the significant variants showed allelic effects in both A549 (adenocarcinoma) and H520 (squamous cell carcinoma) cell lines, a subset of the functional variants displayed a significant cell-type interaction. Transcription factor analyses nominated potential regulators of the functional variants, including those with cell-type-specific expression and those predicted to bind multiple potentially functional variants across the GWAS loci. Linking functional variants to target genes based on four complementary approaches identified candidate susceptibility genes, including those affecting lung cancer cell growth. CRISPR interference of the top functional variant at 20q13.33 validated variant-to-gene connections, including RTEL1, SOX18, and ARFRP1. Our data provide a comprehensive functional analysis of lung cancer GWAS loci and help elucidate the molecular basis of heterogeneity and polygenicity underlying lung cancer susceptibility.
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Affiliation(s)
- Erping Long
- State Key Laboratory of Respiratory Health and Multimorbidity, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Harsh Patel
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Alyxandra Golden
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Michelle Antony
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Jinhu Yin
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Karen Funderburk
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - James Feng
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Lei Song
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Jason W Hoskins
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Laufey T Amundadottir
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Rayjean J Hung
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada
| | - Christopher I Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Jianxin Shi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Nathaniel Rothman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Qing Lan
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Jiyeon Choi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
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9
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Cheng Y, Zhang C, Li Q, Yang X, Chen W, He K, Chen M. MTF1 genetic variants are associated with lung cancer risk in the Chinese Han population. BMC Cancer 2024; 24:778. [PMID: 38943058 PMCID: PMC11212402 DOI: 10.1186/s12885-024-12516-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 06/13/2024] [Indexed: 07/01/2024] Open
Abstract
BACKGROUND Metal-regulatory transcription factor 1 (MTF1), a conserved metal-binding transcription factor in eukaryotes, regulates the proliferation of cancer cells by activating downstream target genes and then participates in the formation and progression of tumors, including lung cancer (LC). The expression level of MTF1 is down-regulated in LC, and high expression of MTF1 is associated with a good prognosis of LC. However, the association between MTF1 polymorphism and LC risk has not been explored. METHODS The genotyping of MTF1 Single nucleotide polymorphisms (SNPs) including rs473279, rs28411034, rs28411352, and rs3748682 was identified by the Agena MassARRAY system among 670 healthy controls and 670 patients with LC. The odds ratio (OR) and 95% confidence intervals (CI) were calculated by logistics regression to assess the association of these SNPs with LC risk. RESULTS MTF1 rs28411034 (OR 1.22, 95% CI 1.03-1.45, p = 0.024) and rs3748682 (OR 1.24, 95% CI 1.04-1.47, p = 0.014) were associated with higher LC susceptibility overall. Moreover, the effect of rs28411034 and rs3748682 on LC susceptibility was observed in males, subjects with body mass index (BMI) ≥ 24 kg/m2, smokers, drinkers, and patients with lung squamous carcinoma (OR and 95% CI > 1, p < 0.05). Besides, rs28411352 (OR 0.73, 95% CI 0.55-0.97, p = 0.028,) showed protective effect for reduced LC risk in drinkers. CONCLUSIONS We were first who reported that rs28411034 and rs3748682 tended to be relevant to increased LC susceptibility among the Chinese Han population. These results of this study could help to recognize the pathogenic mechanisms of the MTF1 gene in LC progress.
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Affiliation(s)
- Yujing Cheng
- Department of Respiratory Medicine, The First Affiliated Hospital of School of Medicine of Xi'an Jiaotong University, Yanta District, No. 277, Yanta West Road, Xi'an, 710061, Shaanxi, China
- Department of Blood Transfusion, The First People's Hospital of Yunnan Province, The Afiliated Hospital of Kunming University of Science and Technology, Kunming, 650032, Yunnan, China
| | - Chan Zhang
- Department of Blood Transfusion, The First People's Hospital of Yunnan Province, The Afiliated Hospital of Kunming University of Science and Technology, Kunming, 650032, Yunnan, China
| | - Qi Li
- Department of Blood Transfusion, The First People's Hospital of Yunnan Province, The Afiliated Hospital of Kunming University of Science and Technology, Kunming, 650032, Yunnan, China
| | - Xin Yang
- Department of Blood Transfusion, The First People's Hospital of Yunnan Province, The Afiliated Hospital of Kunming University of Science and Technology, Kunming, 650032, Yunnan, China
| | - Wanlu Chen
- Department of Blood Transfusion, The First People's Hospital of Yunnan Province, The Afiliated Hospital of Kunming University of Science and Technology, Kunming, 650032, Yunnan, China
| | - KunHua He
- Department of Blood Transfusion, The First People's Hospital of Qujing City, Qujing, 655099, Yunnan, China
| | - Mingwei Chen
- Department of Respiratory Medicine, The First Affiliated Hospital of School of Medicine of Xi'an Jiaotong University, Yanta District, No. 277, Yanta West Road, Xi'an, 710061, Shaanxi, China.
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10
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Panagiotou E, Vathiotis IA, Makrythanasis P, Hirsch F, Sen T, Syrigos K. Biological and therapeutic implications of the cancer-related germline mutation landscape in lung cancer. THE LANCET. RESPIRATORY MEDICINE 2024:S2213-2600(24)00124-3. [PMID: 38885686 DOI: 10.1016/s2213-2600(24)00124-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 04/16/2024] [Accepted: 04/17/2024] [Indexed: 06/20/2024]
Abstract
Although smoking is the primary cause of lung cancer, only about 15% of lifelong smokers develop the disease. Moreover, a substantial proportion of lung cancer cases occur in never-smokers, highlighting the potential role of inherited genetic factors in the cause of lung cancer. Lung cancer is significantly more common among those with a positive family history, especially for early-onset disease. Therefore, the presence of pathogenic germline variants might act synergistically with environmental factors. The incorporation of next-generation sequencing in routine clinical practice has led to the identification of cancer-predisposing mutations in an increasing proportion of patients with lung cancer. This Review summarises the landscape of germline susceptibility in lung cancer and highlights the importance of germline testing in patients diagnosed with the disease, which has the potential to identify individuals at risk, with implications for tailored therapeutic approaches and successful prevention through genetic counselling and screening.
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Affiliation(s)
- Emmanouil Panagiotou
- Third Department of Internal Medicine, Sotiria General Hospital for Chest Diseases, National and Kapodistrian University of Athens, Athens, Greece
| | - Ioannis A Vathiotis
- Third Department of Internal Medicine, Sotiria General Hospital for Chest Diseases, National and Kapodistrian University of Athens, Athens, Greece.
| | - Periklis Makrythanasis
- Laboratory of Medical Genetics, Medical School, National and Kapodistrian University of Athens, Athens, Greece; Department of Genetic Medicine and Development, Medical School, University of Geneva, Geneva, Switzerland; Biomedical Research Foundation of the Academy of Athens, Athens, Greece
| | - Fred Hirsch
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Triparna Sen
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Konstantinos Syrigos
- Third Department of Internal Medicine, Sotiria General Hospital for Chest Diseases, National and Kapodistrian University of Athens, Athens, Greece
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11
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Guan X, Meng X, Zhong G, Zhang Z, Wang C, Xiao Y, Fu M, Zhao H, Zhou Y, Hong S, Xu X, Bai Y, Kan H, Chen R, Wu T, Guo H. Particulate matter pollution, polygenic risk score and mosaic loss of chromosome Y in middle-aged and older men from the Dongfeng-Tongji cohort study. JOURNAL OF HAZARDOUS MATERIALS 2024; 471:134315. [PMID: 38678703 DOI: 10.1016/j.jhazmat.2024.134315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 04/04/2024] [Accepted: 04/14/2024] [Indexed: 05/01/2024]
Abstract
Mosaic loss of chromosome Y (mLOY) is the most common somatic alteration as men aging and may reflect genome instability. PM exposure is a major health concern worldwide, but its effects with genetic factors on mLOY has never been investigated. Here we explored the associations of PM2.5 and PM10 exposure with mLOY of 10,158 males measured via signal intensity of 2186 probes in male-specific chromosome-Y region from Illumina array data. The interactive and joint effects of PM2.5 and PM10 with genetic factors and smoking on mLOY were further evaluated. Compared with the lowest tertiles of PM2.5 levels in each exposure window, the highest tertiles in the same day, 7-, 14-, 21-, and 28-day showed a 0.005, 0.006, 0.007, 0.007, and 0.006 decrease in mLRR-Y, respectively (all P < 0.05), with adjustment for age, BMI, smoking pack-years, alcohol drinking status, physical activity, education levels, season of blood draw, and experimental batch. Such adverse effects were also observed in PM10-mLOY associations. Moreover, the unweighted and weighted PRS presented significant negative associations with mLRR-Y (both P < 0.001). Participants with high PRS and high PM2.5 or PM10 exposure in the 28-day separately showed a 0.018 or 0.019 lower mLRR-Y level [β (95 %CI) = -0.018 (-0.023, -0.012) and - 0.019 (-0.025, -0.014), respectively, both P < 0.001], when compared to those with low PRS and low PM2.5 or PM10 exposure. We also observed joint effects of PM with smoking on exacerbated mLOY. This large study is the first to elucidate the impacts of PM2.5 exposure on mLOY, and provides key evidence regarding the interactive and joint effects of PM with genetic factors on mLOY, which may promote understanding of mLOY development, further modifying and increasing healthy aging in males.
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Affiliation(s)
- Xin Guan
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, China
| | - Xia Meng
- Department of Environment Health, School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and National Health Commission Key Lab of Health Technology Assessment, Fudan University, Shanghai, China
| | - Guorong Zhong
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, China
| | - Zirui Zhang
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, China
| | - Chenming Wang
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, China
| | - Yang Xiao
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, China
| | - Ming Fu
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, China
| | - Hui Zhao
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, China
| | - Yuhan Zhou
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, China
| | - Shiru Hong
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, China
| | - Xuedan Xu
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, China
| | - Yansen Bai
- Institute for Chemical Carcinogenesis, School of Public Health, Guangzhou Medical University, Xinzao, Panyu District, Guangzhou 511436, China
| | - Haidong Kan
- Department of Environment Health, School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and National Health Commission Key Lab of Health Technology Assessment, Fudan University, Shanghai, China
| | - Renjie Chen
- Department of Environment Health, School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and National Health Commission Key Lab of Health Technology Assessment, Fudan University, Shanghai, China
| | - Tangchun Wu
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, China
| | - Huan Guo
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, China.
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12
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Chaturvedi D, Attia Hussein Mahmoud H, Isaac A, Atla RH, Shakeel JN, Heredia M, Marepalli NR, Shukla PS, Gardezi M, Zeeshan M, Ashraf T. Understanding the Cardiovascular Fallout of E-cigarettes: A Comprehensive Review of the Literature. Cureus 2024; 16:e63489. [PMID: 39081430 PMCID: PMC11287103 DOI: 10.7759/cureus.63489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/29/2024] [Indexed: 08/02/2024] Open
Abstract
E-cigarettes (ECs) deliver chemicals, including nicotine. They can cause respiratory distress, addiction, cardiovascular effects, and death. More research is needed, especially regarding their impact on the cardiovascular system (CVS) and during pregnancy. Our article aims to fill this gap by summarizing studies elaborating upon the current impact of ECs and the components thereof on the CVS. Acute respiratory distress outbreaks, nicotine addiction, CVS effects, and deaths have been occasionally reported within this cohort, although these events are not uncommon with neighboring age groups. Randomized control trials implying ECs have some contribution toward quitting smoking have been studied. To regulate EC distribution, the Food and Drug Administration (FDA) and Centers for Disease Control and Prevention (CDC) have created key checkpoints. Additionally, taxation, pricing, age restriction, and media campaigns could be modulated to significantly reduce illicit sales. Education to the users, distributors, and regulators about this product can also play an aiding role in promoting responsible EC use. Another strategy about licensing could be employed, which could incentivize genuine resellers. The effects on CVS and child-bearing by ECs are grim, which calls for strict regulation, awareness, and avoidance by the teetotaler public. They may help individuals stop smoking but not without harming themselves. Strict regulations are necessary to prevent non-judicious use of these devices.
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Affiliation(s)
- Devansh Chaturvedi
- Medicine, Dr Chaturvedi Cancer Hospital and Research Institute, Gorakhpur, IND
- Internal Medicine, King George's Medical University, Lucknow, IND
| | | | - Ashley Isaac
- General Medicine, Isra University Hospital, Hyderabad, PAK
| | - Ragha Harshitha Atla
- Internal Medicine and Obstetrics, Bicol Christian College of Medicine, Ago Medical Center, Legazpi City, PHL
| | | | - Maria Heredia
- Cardiology, Ministry of Public Health of Ecuador, Quito, ECU
| | | | - Pranav S Shukla
- Medicine, Grant Medical College and Sir JJ group of Hospitals, Mumbai, IND
| | - Maira Gardezi
- Internal Medicine, Faisalabad Medical University, Faisalabad, PAK
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13
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Fu M, Meng H, Jiang M, Zhu Z, Guan X, Bai Y, Wang C, Zhou Y, Hong S, Xiao Y, He M, Zhang X, Wang C, Guo H. The interaction effects of zinc and polygenic risk score with benzo[a]pyrene exposure on lung cancer risk: A prospective case-cohort study among Chinese populations. ENVIRONMENTAL RESEARCH 2024; 250:118539. [PMID: 38401684 DOI: 10.1016/j.envres.2024.118539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 02/16/2024] [Accepted: 02/21/2024] [Indexed: 02/26/2024]
Abstract
The relationship of exposure to benzo[a]pyrene (BaP) with lung cancer risk has been firmly established, but whether this association could be modified by other environmental or genetic factors remains to be explored. To investigate whether and how zinc (Zn) and genetic predisposition modify the association between BaP and lung cancer, we performed a case-cohort study with a 5.4-year median follow-up duration, comprising a representative subcohort of 1399 participants and 359 incident lung cancer cases. The baseline concentrations of benzo[a]pyrene diol epoxide-albumin adduct (BPDE-Alb) and Zn were quantified. We also genotyped the participants and computed the polygenic risk score (PRS) for lung cancer. Our findings indicated that elevated BPDE-Alb and PRS were linked to increased lung cancer risk, with the HR (95%CI) of 1.54 (1.36, 1.74) per SD increment in ln-transformed BPDE-Alb and 1.27 (1.14, 1.41) per SD increment in PRS, but high plasma Zn level was linked to a lower lung cancer risk [HR (95%CI)=0.77 (0.66, 0.91) per SD increment in ln-transformed Zn]. There was evidence of effect modification by Zn on BaP-lung cancer association (P for multiplicative interaction = 0.008). As Zn concentrations increased from the lowest to the highest tertile, the lung cancer risk per SD increment in ln-transformed BPDE-Alb decreased from 2.07 (1.48, 2.89) to 1.33 (0.90, 1.95). Additionally, we observed a significant synergistic interaction of BPDE-Alb and PRS [RERI (95%CI) = 0.85 (0.03, 1.67)], with 42% of the incident lung cancer cases among individuals with high BPDE-Alb and high PRS attributable to their additive effect [AP (95%CI) = 0.42 (0.14, 0.69)]. This study provided the first prospective epidemiological evidence that Zn has protective effect against BaP-induced lung tumorigenesis, whereas high genetic risk can enhance the harmful effect of BaP. These findings may provide novel insight into the environment-environment and environment-gene interaction underlying lung cancer development, which may help to develop prevention and intervention strategies to manage BaP-induced lung cancer.
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Affiliation(s)
- Ming Fu
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, China
| | - Hua Meng
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, China
| | - Minghui Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, China
| | - Ziwei Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, China
| | - Xin Guan
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, China
| | - Yansen Bai
- Institute for Chemical Carcinogenesis, School of Public Health, Guangzhou Medical University, Xinzao, Panyu District, Guangzhou, 511416, China
| | - Chenming Wang
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, China
| | - Yuhan Zhou
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, China
| | - Shiru Hong
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, China
| | - Yang Xiao
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, China
| | - Meian He
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, China
| | - Xiaomin Zhang
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, China
| | - Chaolong Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, China
| | - Huan Guo
- Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, 430030, China.
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14
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Tian X, Liu Z. Single nucleotide variants in lung cancer. CHINESE MEDICAL JOURNAL PULMONARY AND CRITICAL CARE MEDICINE 2024; 2:88-94. [PMID: 39169933 PMCID: PMC11332866 DOI: 10.1016/j.pccm.2024.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Indexed: 08/23/2024]
Abstract
Germline genetic variants, including single-nucleotide variants (SNVs) and copy number variants (CNVs), account for interpatient heterogeneity. In the past several decades, genome-wide association studies (GWAS) have identified multiple lung cancer-associated SNVs in Caucasian and Chinese populations. These variants either reside within coding regions and change the structure and function of cancer-related proteins or reside within non-coding regions and alter the expression level of cancer-related proteins. The variants can be used not only for cancer risk assessment and prevention but also for the development of new therapies. In this review, we discuss the lung cancer-associated SNVs identified to date, their contributions to lung tumorigenesis and prognosis, and their potential use in predicting prognosis and implementing therapeutic strategies.
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Affiliation(s)
- Xiaoling Tian
- Zhejiang Key Laboratory of Medical Epigenetics, Department of Cell Biology, School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China
| | - Zhe Liu
- Zhejiang Key Laboratory of Medical Epigenetics, Department of Cell Biology, School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou, Zhejiang 311121, China
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15
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Zou Y, Zhu J, Song C, Li T, Wang K, Shi J, Ye H, Wang P. A polygenetic risk score combined with environmental factors better predict susceptibility to hepatocellular carcinoma in Chinese population. Cancer Med 2024; 13:e7230. [PMID: 38698686 PMCID: PMC11066500 DOI: 10.1002/cam4.7230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 04/11/2024] [Accepted: 04/18/2024] [Indexed: 05/05/2024] Open
Abstract
AIMS This study aimed to investigate environmental factors and genetic variant loci associated with hepatocellular carcinoma (HCC) in Chinese population and construct a weighted genetic risk score (wGRS) and polygenic risk score (PRS). METHODS A case-control study was applied to confirm the single nucleotide polymorphisms (SNPs) and environmental variables linked to HCC in the Chinese population, which had been screened by meta-analyses. wGRS and PRS were built in training sets and validation sets. Area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were applied to evaluate the performance of the models. RESULTS A total of 13 SNPs were included in both risk prediction models. Compared with wGRS, PRS had better accuracy and discrimination ability in predicting HCC risk. The AUC for PRS in combination with drinking history, cirrhosis, HBV infection, and family history of HCC in training sets and validation sets (AUC: 0.86, 95% CI: 0.84-0.89; AUC: 0.85, 95% CI: 0.81-0.89) increased at least 20% than the AUC for PRS alone (AUC: 0.63, 95% CI: 0.60-0.67; AUC: 0.65, 95% CI: 0.60-0.71). CONCLUSIONS A novel model combining PRS with alcohol history, HBV infection, cirrhosis, and family history of HCC could be applied as an effective tool for risk prediction of HCC, which could discriminate at-risk individuals for precise prevention.
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Affiliation(s)
- Yuanlin Zou
- Department of Epidemiology and Statistics, College of Public HealthZhengzhou UniversityZhengzhouHenan ProvinceChina
- Henan Key Laboratory of Tumor Epidemiology and State Key Laboratory of Esophageal Cancer Prevention & TreatmentZhengzhou UniversityZhengzhouHenan ProvinceChina
| | - Jicun Zhu
- Department of PharmacyThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenan ProvinceChina
| | - Caijuan Song
- The Institution for Chronic and Noncommunicable Disease Control and PreventionZhengzhou Center for Disease Control and PreventionZhengzhouHenan ProvinceChina
| | - Tiandong Li
- Department of Epidemiology and Statistics, College of Public HealthZhengzhou UniversityZhengzhouHenan ProvinceChina
- Henan Key Laboratory of Tumor Epidemiology and State Key Laboratory of Esophageal Cancer Prevention & TreatmentZhengzhou UniversityZhengzhouHenan ProvinceChina
| | - Keyan Wang
- Henan Key Laboratory of Tumor Epidemiology and State Key Laboratory of Esophageal Cancer Prevention & TreatmentZhengzhou UniversityZhengzhouHenan ProvinceChina
- Henan Institute of Medical and Pharmaceutical SciencesZhengzhou UniversityZhengzhouHenan ProvinceChina
| | - Jianxiang Shi
- Henan Key Laboratory of Tumor Epidemiology and State Key Laboratory of Esophageal Cancer Prevention & TreatmentZhengzhou UniversityZhengzhouHenan ProvinceChina
- Henan Institute of Medical and Pharmaceutical SciencesZhengzhou UniversityZhengzhouHenan ProvinceChina
| | - Hua Ye
- Department of Epidemiology and Statistics, College of Public HealthZhengzhou UniversityZhengzhouHenan ProvinceChina
- Henan Key Laboratory of Tumor Epidemiology and State Key Laboratory of Esophageal Cancer Prevention & TreatmentZhengzhou UniversityZhengzhouHenan ProvinceChina
| | - Peng Wang
- Department of Epidemiology and Statistics, College of Public HealthZhengzhou UniversityZhengzhouHenan ProvinceChina
- Henan Key Laboratory of Tumor Epidemiology and State Key Laboratory of Esophageal Cancer Prevention & TreatmentZhengzhou UniversityZhengzhouHenan ProvinceChina
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Bian L, Ma Z, Fu X, Ji C, Wang T, Yan C, Dai J, Ma H, Hu Z, Shen H, Wang L, Zhu M, Jin G. Associations of combined phenotypic aging and genetic risk with incident cancer: A prospective cohort study. eLife 2024; 13:RP91101. [PMID: 38687190 PMCID: PMC11060710 DOI: 10.7554/elife.91101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024] Open
Abstract
Background Age is the most important risk factor for cancer, but aging rates are heterogeneous across individuals. We explored a new measure of aging-Phenotypic Age (PhenoAge)-in the risk prediction of site-specific and overall cancer. Methods Using Cox regression models, we examined the association of Phenotypic Age Acceleration (PhenoAgeAccel) with cancer incidence by genetic risk group among 374,463 participants from the UK Biobank. We generated PhenoAge using chronological age and nine biomarkers, PhenoAgeAccel after subtracting the effect of chronological age by regression residual, and an incidence-weighted overall cancer polygenic risk score (CPRS) based on 20 cancer site-specific polygenic risk scores (PRSs). Results Compared with biologically younger participants, those older had a significantly higher risk of overall cancer, with hazard ratios (HRs) of 1.22 (95% confidence interval, 1.18-1.27) in men, and 1.26 (1.22-1.31) in women, respectively. A joint effect of genetic risk and PhenoAgeAccel was observed on overall cancer risk, with HRs of 2.29 (2.10-2.51) for men and 1.94 (1.78-2.11) for women with high genetic risk and older PhenoAge compared with those with low genetic risk and younger PhenoAge. PhenoAgeAccel was negatively associated with the number of healthy lifestyle factors (Beta = -1.01 in men, p<0.001; Beta = -0.98 in women, p<0.001). Conclusions Within and across genetic risk groups, older PhenoAge was consistently related to an increased risk of incident cancer with adjustment for chronological age and the aging process could be retarded by adherence to a healthy lifestyle. Funding This work was supported by the National Natural Science Foundation of China (82230110, 82125033, 82388102 to GJ; 82273714 to MZ); and the Excellent Youth Foundation of Jiangsu Province (BK20220100 to MZ).
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Affiliation(s)
- Lijun Bian
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health Nanjing Medical UniversityNanjingChina
| | - Zhimin Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
| | - Xiangjin Fu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
| | - Chen Ji
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
| | - Tianpei Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
| | - Caiwang Yan
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health Nanjing Medical UniversityNanjingChina
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi Medical Center, Nanjing Medical UniversityWuxiChina
| | - Juncheng Dai
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health Nanjing Medical UniversityNanjingChina
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health Nanjing Medical UniversityNanjingChina
| | - Zhibin Hu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health Nanjing Medical UniversityNanjingChina
| | - Hongbing Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health Nanjing Medical UniversityNanjingChina
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical SciencesBeijingChina
| | - Lu Wang
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi Medical Center, Nanjing Medical UniversityWuxiChina
| | - Meng Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health Nanjing Medical UniversityNanjingChina
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi Medical Center, Nanjing Medical UniversityWuxiChina
| | - Guangfu Jin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical UniversityNanjingChina
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine and China International Cooperation Center for Environment and Human Health Nanjing Medical UniversityNanjingChina
- Department of Chronic Non-Communicable Disease Control, The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi Medical Center, Nanjing Medical UniversityWuxiChina
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Duncan MS, Diaz-Zabala H, Jaworski J, Tindle HA, Greevy RA, Lipworth L, Hung RJ, Freiberg MS, Aldrich MC. Interaction between Continuous Pack-Years Smoked and Polygenic Risk Score on Lung Cancer Risk: Prospective Results from the Framingham Heart Study. Cancer Epidemiol Biomarkers Prev 2024; 33:500-508. [PMID: 38227004 PMCID: PMC10988206 DOI: 10.1158/1055-9965.epi-23-0571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 10/13/2023] [Accepted: 01/11/2024] [Indexed: 01/17/2024] Open
Abstract
BACKGROUND Lung cancer risk attributable to smoking is dose dependent, yet few studies examining a polygenic risk score (PRS) by smoking interaction have included comprehensive lifetime pack-years smoked. METHODS We analyzed data from participants of European ancestry in the Framingham Heart Study Original (n = 454) and Offspring (n = 2,470) cohorts enrolled in 1954 and 1971, respectively, and followed through 2018. We built a PRS for lung cancer using participant genotyping data and genome-wide association study summary statistics from a recent study in the OncoArray Consortium. We used Cox proportional hazards regression models to assess risk and the interaction between pack-years smoked and genetic risk for lung cancer adjusting for European ancestry, age, sex, and education. RESULTS We observed a significant submultiplicative interaction between pack-years and PRS on lung cancer risk (P = 0.09). Thus, the relative risk associated with each additional 10 pack-years smoked decreased with increasing genetic risk (HR = 1.56 at one SD below mean PRS, HR = 1.48 at mean PRS, and HR = 1.40 at one SD above mean PRS). Similarly, lung cancer risk per SD increase in the PRS was highest among those who had never smoked (HR = 1.55) and decreased with heavier smoking (HR = 1.32 at 30 pack-years). CONCLUSIONS These results suggest the presence of a submultiplicative interaction between pack-years and genetics on lung cancer risk, consistent with recent findings. Both smoking and genetics were significantly associated with lung cancer risk. IMPACT These results underscore the contributions of genetics and smoking on lung cancer risk and highlight the negative impact of continued smoking regardless of genetic risk.
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Affiliation(s)
- Meredith S. Duncan
- Department of Biostatistics, University of Kentucky, Lexington, Kentucky
- Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Hector Diaz-Zabala
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - James Jaworski
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Hilary A. Tindle
- Geriatric Research Education and Clinical Centers (GRECC), Veterans Affairs Tennessee Valley Healthcare System, Nashville, Tennessee
- Division of Internal Medicine, Vanderbilt University Medical Center, Nashville Tennessee
| | - Robert A. Greevy
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Loren Lipworth
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Rayjean J. Hung
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Matthew S. Freiberg
- Division of Cardiovascular Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Geriatric Research Education and Clinical Centers (GRECC), Veterans Affairs Tennessee Valley Healthcare System, Nashville, Tennessee
| | - Melinda C. Aldrich
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
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Sambou ML, Zhao X, Hong T, Wang N, Dai J. Associations between sleep-behavioral traits and healthspan: A one-sample Mendelian randomization study based on 388,909 participants of the UK-Biobank. J Affect Disord 2024; 350:854-862. [PMID: 38262521 DOI: 10.1016/j.jad.2024.01.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 09/15/2023] [Accepted: 01/10/2024] [Indexed: 01/25/2024]
Abstract
BACKGROUND Although the association between sleep behavior and morbidity and mortality risk has been reported before, there is still uncertainty whether the observed associations are causal or confounding. Therefore, we investigated the causal relationships between sleep-behavioral traits and terminated healthspan risk using Mendelian randomization (MR). METHODS We conducted a one-sample MR analysis to evaluate causality between six sleep-behavioral traits (sleep duration, chronotype/morningness, napping, sleeplessness/insomnia, and getting up from bed) and risk of healthspan termination among 388, 909 UK Biobank (UKB) participants. Instrumental variables for sleep behaviors (N = 590) were obtained from recent genome-wide association studies (GWAS). We defined healthspan based on eight predominant health-terminating events associated with longevity (congestive heart failure, myocardial infarction, chronic obstructive pulmonary disease, stroke, dementia, diabetes, cancer, and death). We further constructed a sleep score and a weighted genetic risk score to increase the predictive ability of the sleep-behavioral traits. Cox regression models and Inverse Probability Treatment Weighting (IPTW) were implemented, followed by MR to assess causation. We used inverse-variance-weighted MR to estimate causal effects, and weighted-median and MR-egger for sensitivity analysis to test the pleiotropic effects. RESULTS In IPTW, we observed a decreased risk of terminated healthspan for healthy sleep behaviors such as 'sleep duration 7-8h/d' (Hazard ratio, HR = 0.93; 95 % confidence interval, CI: 0.92-0.96; P < 0.001); 'morningness' (HR = 0.95; 95%CI: 0.93-0.98; P < 0.01); 'napping' (HR = 0.93; 95%CI: 0.91-0.94; P < 0.001); 'easy getting up from bed' (HR = 0.91; 95%CI: 0.88-0.93; P < 0.001); and, 'never/rarely experience sleeplessness/insomnia' (HR = 0.94; 95%CI: 0.92-0.96; P < 0.001). MR results further indicated causal associations between healthy sleep duration (OR = 0.98; 95%CI: 0.97-1.00; P = 0.036) and insomnia (OR = 1.02; 95%CI: 1.01-1.03; P < 0.001) with terminated healthspan. MR-egger did not suggest any potential pleiotropy. CONCLUSION This study supports abnormal sleep duration and insomnia as potential causal risk factors for terminated healthspan. Thus, healthy sleep behavior is valuable for the extension of healthspan, and well-designed and tailored sleep health interventions are warranted.
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Affiliation(s)
- Muhammed Lamin Sambou
- Department of Epidemiology and Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Xiaoyu Zhao
- Department of Epidemiology and Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Tongtong Hong
- Department of Epidemiology and Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Nanxi Wang
- Department of Epidemiology and Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Juncheng Dai
- Department of Epidemiology and Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing 211166, China.
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Xu L, Gan T, Chen P, Liu Y, Qu S, Shi S, Liu L, Zhou X, Lv J, Zhang H. Clinical Application of Polygenic Risk Score in IgA Nephropathy. PHENOMICS (CHAM, SWITZERLAND) 2024; 4:146-157. [PMID: 38884057 PMCID: PMC11169313 DOI: 10.1007/s43657-023-00138-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 10/11/2023] [Accepted: 10/18/2023] [Indexed: 06/18/2024]
Abstract
Genome-wide association studies (GWASs) have identified 30 independent genetic variants associated with IgA nephropathy (IgAN). A genetic risk score (GRS) represents the number of risk alleles carried and thus captures an individual's genetic risk. However, whether and which polygenic risk score crucial for the evaluation of any potential personal or clinical utility on risk and prognosis are still obscure. We constructed different GRS models based on different sets of variants, which were top single nucleotide polymorphisms (SNPs) reported in the previous GWASs. The case-control GRS analysis included 3365 IgAN patients and 8842 healthy individuals. The association between GRS and clinical variability, including age at diagnosis, clinical parameters, Oxford pathology classification, and kidney prognosis was further evaluated in a prospective cohort of 1747 patients. Three GRS models (15 SNPs, 21 SNPs, and 55 SNPs) were constructed after quality control. The patients with the top 20% GRS had 2.42-(15 SNPs, p = 8.12 × 10-40), 3.89-(21 SNPs, p = 3.40 × 10-80) and 3.73-(55 SNPs, p = 6.86 × 10-81) fold of risk to develop IgAN compared to the patients with the bottom 20% GRS, with area under the receiver operating characteristic curve (AUC) of 0.59, 0.63, and 0.63 in group discriminations, respectively. A positive correlation between GRS and microhematuria, mesangial hypercellularity, segmental glomerulosclerosis and a negative correlation on the age at diagnosis, body mass index (BMI), mean arterial pressure (MAP), serum C3, triglycerides can be observed. Patients with the top 20% GRS also showed a higher risk of worse prognosis for all three models (1.36, 1.42, and 1.36 fold of risk) compared to the remaining 80%, whereas 21 SNPs model seemed to show a slightly better fit in prediction. Collectively, a higher burden of risk variants is associated with earlier disease onset and a higher risk of a worse prognosis. This may be informational in translating knowledge on IgAN genetics into disease risk prediction and patient stratification. Supplementary Information The online version contains supplementary material available at 10.1007/s43657-023-00138-6.
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Affiliation(s)
- Linlin Xu
- Renal Division, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034 People's Republic of China
- Kidney Genetics Center, Peking University Institute of Nephrology, Beijing, 100034 People's Republic of China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, 100034 People's Republic of China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, 100034 People's Republic of China
| | - Ting Gan
- Renal Division, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034 People's Republic of China
- Kidney Genetics Center, Peking University Institute of Nephrology, Beijing, 100034 People's Republic of China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, 100034 People's Republic of China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, 100034 People's Republic of China
| | - Pei Chen
- Renal Division, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034 People's Republic of China
- Kidney Genetics Center, Peking University Institute of Nephrology, Beijing, 100034 People's Republic of China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, 100034 People's Republic of China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, 100034 People's Republic of China
| | - Yang Liu
- Renal Division, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034 People's Republic of China
- Kidney Genetics Center, Peking University Institute of Nephrology, Beijing, 100034 People's Republic of China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, 100034 People's Republic of China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, 100034 People's Republic of China
| | - Shu Qu
- Renal Division, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034 People's Republic of China
- Kidney Genetics Center, Peking University Institute of Nephrology, Beijing, 100034 People's Republic of China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, 100034 People's Republic of China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, 100034 People's Republic of China
| | - Sufang Shi
- Renal Division, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034 People's Republic of China
- Kidney Genetics Center, Peking University Institute of Nephrology, Beijing, 100034 People's Republic of China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, 100034 People's Republic of China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, 100034 People's Republic of China
| | - Lijun Liu
- Renal Division, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034 People's Republic of China
- Kidney Genetics Center, Peking University Institute of Nephrology, Beijing, 100034 People's Republic of China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, 100034 People's Republic of China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, 100034 People's Republic of China
| | - Xujie Zhou
- Renal Division, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034 People's Republic of China
- Kidney Genetics Center, Peking University Institute of Nephrology, Beijing, 100034 People's Republic of China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, 100034 People's Republic of China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, 100034 People's Republic of China
| | - Jicheng Lv
- Renal Division, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034 People's Republic of China
- Kidney Genetics Center, Peking University Institute of Nephrology, Beijing, 100034 People's Republic of China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, 100034 People's Republic of China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, 100034 People's Republic of China
| | - Hong Zhang
- Renal Division, Peking University First Hospital, No. 8, Xishiku Street, Xicheng District, Beijing, 100034 People's Republic of China
- Kidney Genetics Center, Peking University Institute of Nephrology, Beijing, 100034 People's Republic of China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing, 100034 People's Republic of China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing, 100034 People's Republic of China
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Zhang E, Sun Q, Zhang C, Ma H, Zhang J, Ding Y, Wang G, Jin C, Jin C, Fu Y, Yan C, Zhu M, Wang C, Dai J, Jin G, Hu Z, Shen H, Ma H. Comprehensive functional interrogation of susceptibility loci in GWASs identified KIAA0391 as a novel oncogenic driver via regulating pyroptosis in NSCLC. Cancer Lett 2024; 585:216646. [PMID: 38262497 DOI: 10.1016/j.canlet.2024.216646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/23/2023] [Accepted: 01/05/2024] [Indexed: 01/25/2024]
Abstract
Approximately 51 non-small-cell lung cancer (NSCLC) risk loci have been identified by genome-wide association studies (GWASs). We conducted a high throughput RNA-interference (RNAi) screening to identify the candidate causal genes in NSCLC risk loci. KIAA0391 at 14q13.1 had the highest score and could promote proliferation and metastasis of NSCLC in vitro and in vivo. We next prioritized rs3783313 as a causal variant at 14q13.1, by integrating a large-scale population study consisting of 27,120 lung cancer cases and 27,355 controls, functional annotation, and expression quantitative trait locus (eQTL) analysis. Then we found that rs3783313 could facilitate a promoter-enhancer interaction to upregulate KIAA0391 expression by affecting the affinity of transcription factor NFYA. Mechanistically, KIAA0391 knockdown dramatically influenced pyroptosis-related pathways and increased the expression of CASP1. And KIAA0391 transcriptionally repressed CASP1 by binding to SMAD2 and induced an anti-pyroptosis phenotype, promoting tumorigenesis of NSCLC, which provides new insights and potential target for NSCLC.
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Affiliation(s)
- Erbao Zhang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Qi Sun
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Chang Zhang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China; The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou Second People's Hospital, Changzhou Medical Center, Nanjing Medical University, Nanjing 211166, China
| | - Huimin Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Jing Zhang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Yue Ding
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Guoqing Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Chen Jin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Chenying Jin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Yating Fu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Caiwang Yan
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Meng Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Cheng Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Juncheng Dai
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Guangfu Jin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Zhibin Hu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
| | - Hongbing Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China; Research Unit of Prospective Cohort of Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing 100142, China.
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China; Research Unit of Prospective Cohort of Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing 100142, China.
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21
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Sun C, Cheng X, Xu J, Chen H, Tao J, Dong Y, Wei S, Chen R, Meng X, Ma Y, Tian H, Guo X, Bi S, Zhang C, Kang J, Zhang M, Lv H, Shang Z, Lv W, Zhang R, Jiang Y. A review of disease risk prediction methods and applications in the omics era. Proteomics 2024:e2300359. [PMID: 38522029 DOI: 10.1002/pmic.202300359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 03/08/2024] [Accepted: 03/12/2024] [Indexed: 03/25/2024]
Abstract
Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.
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Affiliation(s)
- Chen Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Xiangshu Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Jing Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Haiyan Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Junxian Tao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Yu Dong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Siyu Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Rui Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xin Meng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yingnan Ma
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Hongsheng Tian
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xuying Guo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shuo Bi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chen Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jingxuan Kang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Mingming Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hongchao Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zhenwei Shang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Wenhua Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Ruijie Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yongshuai Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
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22
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Li Y, Xiao X, Li J, Han Y, Cheng C, Fernandes GF, Slewitzke SE, Rosenberg SM, Zhu M, Byun J, Bossé Y, McKay JD, Albanes D, Lam S, Tardon A, Chen C, Bojesen SE, Landi MT, Johansson M, Risch A, Bickeböller H, Wichmann HE, Christiani DC, Rennert G, Arnold SM, Goodman GE, Field JK, Davies MP, Shete S, Marchand LL, Liu G, Hung RJ, Andrew AS, Kiemeney LA, Sun R, Zienolddiny S, Grankvist K, Johansson M, Caporaso NE, Cox A, Hong YC, Lazarus P, Schabath MB, Aldrich MC, Schwartz AG, Gorlov I, Purrington KS, Yang P, Liu Y, Bailey-Wilson JE, Pinney SM, Mandal D, Willey JC, Gaba C, Brennan P, Xia J, Shen H, Amos CI. Lung Cancer in Ever- and Never-Smokers: Findings from Multi-Population GWAS Studies. Cancer Epidemiol Biomarkers Prev 2024; 33:389-399. [PMID: 38180474 PMCID: PMC10905670 DOI: 10.1158/1055-9965.epi-23-0613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 08/03/2023] [Accepted: 01/03/2024] [Indexed: 01/06/2024] Open
Abstract
BACKGROUND Clinical, molecular, and genetic epidemiology studies displayed remarkable differences between ever- and never-smoking lung cancer. METHODS We conducted a stratified multi-population (European, East Asian, and African descent) association study on 44,823 ever-smokers and 20,074 never-smokers to identify novel variants that were missed in the non-stratified analysis. Functional analysis including expression quantitative trait loci (eQTL) colocalization and DNA damage assays, and annotation studies were conducted to evaluate the functional roles of the variants. We further evaluated the impact of smoking quantity on lung cancer risk for the variants associated with ever-smoking lung cancer. RESULTS Five novel independent loci, GABRA4, intergenic region 12q24.33, LRRC4C, LINC01088, and LCNL1 were identified with the association at two or three populations (P < 5 × 10-8). Further functional analysis provided multiple lines of evidence suggesting the variants affect lung cancer risk through excessive DNA damage (GABRA4) or cis-regulation of gene expression (LCNL1). The risk of variants from 12 independent regions, including the well-known CHRNA5, associated with ever-smoking lung cancer was evaluated for never-smokers, light-smokers (packyear ≤ 20), and moderate-to-heavy-smokers (packyear > 20). Different risk patterns were observed for the variants among the different groups by smoking behavior. CONCLUSIONS We identified novel variants associated with lung cancer in only ever- or never-smoking groups that were missed by prior main-effect association studies. IMPACT Our study highlights the genetic heterogeneity between ever- and never-smoking lung cancer and provides etiologic insights into the complicated genetic architecture of this deadly cancer.
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Affiliation(s)
- Yafang Li
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Xiangjun Xiao
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
| | - Jianrong Li
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
| | - Younghun Han
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Chao Cheng
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Gail F. Fernandes
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas
| | - Shannon E. Slewitzke
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas
| | - Susan M. Rosenberg
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas
| | - Meng Zhu
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, P.R. China
| | - Jinyoung Byun
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Yohan Bossé
- Institut universitaire de cardiologie et de pneumologie de Québec, Department of Molecular Medicine, Laval University, Quebec City, Canada
| | - James D. McKay
- Section of Genetics, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Demetrios Albanes
- Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland
| | - Stephen Lam
- Department of Integrative Oncology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Adonina Tardon
- Public Health Department, University of Oviedo, ISPA and CIBERESP, Asturias, Spain
| | - Chu Chen
- Program in Epidemiology, Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Stig E. Bojesen
- Department of Clinical Biochemistry, Copenhagen University Hospital, Copenhagen, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Maria T. Landi
- Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland
| | - Mattias Johansson
- Section of Genetics, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Angela Risch
- Thoraxklinik at University Hospital Heidelberg, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC-H), Heidelberg, Germany
- University of Salzburg and Cancer Cluster Salzburg, Salzburg, Austria
| | - Heike Bickeböller
- Department of Genetic Epidemiology, University Medical Center, Georg-August-University Göttingen, Göttingen, Germany
| | | | - David C. Christiani
- Departments of Environmental Health and Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts
| | - Gad Rennert
- Clalit National Cancer Control Center at Carmel Medical Center and Technion Faculty of Medicine, Haifa, Israel
| | | | | | - John K. Field
- Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
| | - Michael P.A. Davies
- Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
| | - Sanjay Shete
- Department of Biostatistics, The University of Texas, MD Anderson Cancer Center, Houston, Texas
- Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Loïc Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii
| | - Geoffrey Liu
- University Health Network- The Princess Margaret Cancer Centre, Toronto, California
| | - Rayjean J. Hung
- Luenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada
- Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Canada
| | - Angeline S. Andrew
- Departments of Epidemiology and Community and Family Medicine, Dartmouth College, Hanover, New Hampshire
| | | | - Ryan Sun
- Department of Biostatistics, The University of Texas, MD Anderson Cancer Center, Houston, Texas
| | | | - Kjell Grankvist
- Department of Medical Biosciences, Umeå University, Umeå, Sweden
| | | | - Neil E. Caporaso
- Division of Cancer Epidemiology and Genetics, NCI, NIH, Bethesda, Maryland
| | - Angela Cox
- Department of Oncology and Metabolism, University of Sheffield, Sheffield, United Kingdom
| | - Yun-Chul Hong
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, Republic of South Korea
| | - Philip Lazarus
- Department of Pharmaceutical Sciences, College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington
| | - Matthew B. Schabath
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Melinda C. Aldrich
- Department of Thoracic Surgery, Division of Epidemiology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Ann G. Schwartz
- Department of Oncology, Wayne State University School of Medicine, Detroit, Michigan
- Karmanos Cancer Institute, Detroit, Michigan
| | - Ivan Gorlov
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | - Kristen S. Purrington
- Department of Oncology, Wayne State University School of Medicine, Detroit, Michigan
- Karmanos Cancer Institute, Detroit, Michigan
| | - Ping Yang
- Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
| | - Yanhong Liu
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
| | | | - Susan M. Pinney
- University of Cincinnati College of Medicine, Cincinnati, Ohio
| | - Diptasri Mandal
- Louisiana State University Health Sciences Center, New Orleans, Louisiana
| | - James C. Willey
- College of Medicine and Life Sciences, University of Toledo, Toledo, Ohio
| | - Colette Gaba
- The University of Toledo College of Medicine, Toledo, Ohio
| | - Paul Brennan
- Institut universitaire de cardiologie et de pneumologie de Québec, Department of Molecular Medicine, Laval University, Quebec City, Canada
| | - Jun Xia
- Creighton University School of Medicine, Omaha, Nebraska
| | - Hongbing Shen
- Department of Epidemiology and Biostatistics, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing, P.R. China
| | - Christopher I. Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, Texas
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, Texas
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas
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23
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Wei X, Sun D, Gao J, Zhang J, Zhu M, Yu C, Ma Z, Fu Y, Ji C, Pei P, Yang L, Millwood IY, Walters RG, Chen Y, Du H, Jin G, Chen Z, Hu Z, Li L, Shen H, Lv J, Ma H. Development and evaluation of a polygenic risk score for lung cancer in never-smoking women: A large-scale prospective Chinese cohort study. Int J Cancer 2024; 154:807-815. [PMID: 37846649 DOI: 10.1002/ijc.34765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 08/30/2023] [Accepted: 09/13/2023] [Indexed: 10/18/2023]
Abstract
The proportion of lung cancer in never smokers is rising, especially among Asian women, but there is no effective early detection tool. Here, we developed a polygenic risk score (PRS), which may help to identify the population with higher risk of lung cancer in never-smoking women. We first performed a large GWAS meta-analysis (8595 cases and 8275 controls) to systematically identify the susceptibility loci for lung cancer in never-smoking Asian women and then generated a PRS using GWAS datasets. Furthermore, we evaluated the utility and effectiveness of PRS in an independent Chinese prospective cohort comprising 55 266 individuals. The GWAS meta-analysis identified eight known loci and a novel locus (5q11.2) at the genome-wide statistical significance level of P < 5 × 10-8 . Based on the summary statistics of GWAS, we derived a polygenic risk score including 21 variants (PRS-21) for lung cancer in never-smoking women. Furthermore, PRS-21 had a hazard ratio (HR) per SD of 1.29 (95% CI = 1.18-1.41) in the prospective cohort. Compared with participants who had a low genetic risk, those with an intermediate (HR = 1.32, 95% CI: 1.00-1.72) and high (HR = 2.09, 95% CI: 1.56-2.80) genetic risk had a significantly higher risk of incident lung cancer. The addition of PRS-21 to the conventional risk model yielded a modest significant improvement in AUC (0.697 to 0.711) and net reclassification improvement (24.2%). The GWAS-derived PRS-21 significantly improves the risk stratification and prediction accuracy for incident lung cancer in never-smoking Asian women, demonstrating the potential for identification of high-risk individuals and early screening.
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Affiliation(s)
- Xiaoxia Wei
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Jiaxin Gao
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Jing Zhang
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Meng Zhu
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Zhimin Ma
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yating Fu
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Chen Ji
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
| | - Iona Y Millwood
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
| | - Robin G Walters
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
| | - Guangfu Jin
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, UK
| | - Zhibin Hu
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- State Key Laboratory of Reproductive Medicine, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Hongbing Shen
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Hongxia Ma
- Department of Epidemiology and Biostatistics, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, China
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24
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Xia C, Xu Y, Li H, He S, Chen W. Benefits and harms of polygenic risk scores in organised cancer screening programmes: a cost-effectiveness analysis. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2024; 44:101012. [PMID: 38304718 PMCID: PMC10832505 DOI: 10.1016/j.lanwpc.2024.101012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 12/18/2023] [Accepted: 01/07/2024] [Indexed: 02/03/2024]
Abstract
Background While polygenic risk scores (PRS) could enable the streamlining of organised cancer screening programmes, its current discriminative ability is limited. We conducted a cost-effectiveness analysis to trade-off the benefits and harms of PRS-stratified cancer screening in China. Methods The validated National Cancer Center (NCC) modelling framework for six cancers (lung, liver, breast, gastric, colorectum, and oesophagus) was used to simulate cancer incidence, progression, stage-specific cancer detection, and risk of death. We estimated the number of cancer deaths averted, quality-adjusted life-years (QALY) gained, number needed to screen (NNS), overdiagnosis, and incremental cost-effectiveness ratio (ICER) of one-time PRS-stratified screening strategy (screening 25% of PRS-defined high-risk population) for a birth cohort at age 60 in 2025, compared with unstratified screening strategy (screening 25% of general population) and no screening strategy. We applied lifetime horizon, societal perspective, and 3% discount rate. An ICER less than $18,364 per QALY gained is considered cost-effective. Findings One-time cancer screening for population aged 60 was the most cost-effective strategy compared to screening at other ages. Compared with an unstratified screening strategy, the PRS-stratified screening strategy averted more cancer deaths (61,237 vs. 40,329), had a lower NNS to prevent one death (307 vs. 451), had a slightly higher overdiagnosis (14.1% vs. 13.8%), and associated with an additional 130,045 QALYs at an additional cost of $1942 million, over a lifetime horizon. The ICER for all six cancers combined was $14,930 per QALY gained, with the ICER varying from $7928 in colorectal cancer to $39,068 in liver cancer. ICER estimates were sensitive to changes in risk threshold and cost of PRS tools. Interpretation PRS-stratified screening strategy modestly improves clinical benefit and cost-effectiveness of organised cancer screening programmes. Reducing the costs of polygenic risk stratification is needed before PRS implementation. Funding The Chinese Academy of Medical Sciences, the Jing-jin-ji Special Projects for Basic Research Cooperation, and the Sanming Project of the Medicine in Shenzhen.
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Affiliation(s)
- Changfa Xia
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yongjie Xu
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - He Li
- Office of National Cancer Regional Medical Centre in Liaoning Province, The First Affiliated Hospital of China Medical University, Shenyang, China
| | - Siyi He
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wanqing Chen
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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25
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Hua T, Zhang C, Fu Y, Qin N, Liu S, Chen C, Gong L, Ma H, Ding Y, Wei X, Jin C, Jin C, Zhu M, Zhang E, Dai J, Ma H. Integrative analyses of N6-methyladenosine-associated single-nucleotide polymorphisms (m6A-SNPs) identify tumor suppressor gene AK9 in lung cancer. Mol Carcinog 2024; 63:538-548. [PMID: 38051288 DOI: 10.1002/mc.23669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 11/21/2023] [Accepted: 11/25/2023] [Indexed: 12/07/2023]
Abstract
N6 -methyladenosine (m6 A) modification has been identified as one of the most important epigenetic regulation mechanisms in the development of human cancers. However, the association between m6 A-associated single-nucleotide polymorphisms (m6 A-SNPs) and lung cancer risk remains largely unknown. Here, we identified m6 A-SNPs and examined the association of these m6 A-SNPs with lung cancer risk in 13,793 lung cancer cases and 14,027 controls. In silico functional annotation was used to identify causal m6 A-SNPs and target genes. Furthermore, methylated RNA immunoprecipitation and quantitative real-time polymerase chain reaction (MeRIP-qPCR) assay was performed to assess the m6 A modification level of different genotypes of the causal SNP. In vitro assays were performed to validate the potential role of the target gene in lung cancer. A total of 8794 m6 A-SNPs were detected, among which 397 SNPs in nine susceptibility loci were associated with lung cancer risk, including six novel loci. Bioinformatics analyses indicated that rs1321328 in 6q21 was located around the m6 A modification site of AK9 and significantly reduced AK9 expression (β = -0.15, p = 2.78 × 10-8 ). Moreover, AK9 was significantly downregulated in lung cancer tissues than that in adjacent normal tissues of samples from the Cancer Genome Atlas and Nanjing Lung Cancer Cohort. MeRIP-qPCR assay suggested that C allele of rs1321328 could significantly decrease the m6 A modification level of AK9 compared with G allele. In vitro assays verified the tumor-suppressing role of AK9 in lung cancer. These findings shed light on the pathogenic mechanism of lung cancer susceptibility loci linked with m6 A modification.
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Affiliation(s)
- Tingting Hua
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Chang Zhang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- The Affiliated Changzhou Second People's Hospital of Nanjing Medical University, Changzhou Second People's Hospital, Changzhou Medical Center, Nanjing Medical University, Nanjing, China
| | - Yating Fu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Na Qin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Su Liu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Congcong Chen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Linnan Gong
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Huimin Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yue Ding
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Xiaoxia Wei
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Chenying Jin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Chen Jin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Meng Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Erbao Zhang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Juncheng Dai
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, China
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Huang YC, Lee MC, Huang SY, Chou CM, Yang HW, Chen IC. Polygenic Risk Score in Predicting Esophageal, Oropharyngeal, and Hypopharynx Cancer Risk among Taiwanese Population. Cancers (Basel) 2024; 16:707. [PMID: 38398100 PMCID: PMC10886704 DOI: 10.3390/cancers16040707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/02/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
Esophageal cancer shares strong associations with oropharyngeal and hypopharyngeal cancers, primarily due to shared risk factors like excessive tobacco and alcohol use. This retrospective study at Taichung Veterans General Hospital involved 54,692 participants, including 385 with squamous cell carcinoma (SCC) of the esophagus, oropharynx, or hypopharynx. Using a polygenic risk score (PRS) derived from 8353 single-nucleotide polymorphisms, researchers aimed to assess its correlation with cancer incidence and prognosis. The study found a 1.83-fold higher risk of esophageal, oropharyngeal, and hypopharyngeal SCCs in participants with a high PRS (Q4) compared to the low-PRS group (Q1). Esophageal cancer risk demonstrated a significant positive association with the PRS, as did hypopharyngeal cancer. Clinical parameters and staging showed limited associations with PRS quartiles, and the PRS did not significantly impact recurrence or mortality rates. The research highlighted that a higher PRS is linked to increased susceptibility to esophageal and hypopharyngeal cancer. Notably, a specific polygenic risk score, PGS001087, exhibited a discernible association with SCC risk, particularly in specific subtypes and advanced disease stages. However, it was not significantly linked to clinical cancer staging, emphasizing the multifactorial nature of cancer development. This hospital study reveals that a higher PRS correlates with increased susceptibility to esophageal and hypopharyngeal cancers. Notably, PGS001087 shows a discernible association with SCC risk in specific subtypes and advanced stages, although not significantly linked to clinical cancer staging. These findings enhance our understanding of genetic factors in upper aerodigestive tract cancers, particularly esophageal SCC, guiding future research and risk assessment strategies.
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Affiliation(s)
- Yu-Che Huang
- Division of Thoracic Surgery, Department of Surgery, Taichung Veterans General Hospital, Taichung 40705, Taiwan;
- Department of Critical Care Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan
| | - Ming-Ching Lee
- Division of Thoracic Surgery, Department of Surgery, Taichung Veterans General Hospital, Taichung 40705, Taiwan;
- Department of Medical Education, Taichung Veterans General Hospital, Taichung 40705, Taiwan
| | - Sheng-Yang Huang
- Division of Pediatric Surgery, Department of Surgery, Taichung Veterans General Hospital, Taichung 40705, Taiwan; (S.-Y.H.); (C.-M.C.)
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung 40227, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 11267, Taiwan
| | - Chia-Man Chou
- Division of Pediatric Surgery, Department of Surgery, Taichung Veterans General Hospital, Taichung 40705, Taiwan; (S.-Y.H.); (C.-M.C.)
- Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung 40227, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei 11267, Taiwan
| | - Hui-Wen Yang
- Department of Medical Research, Taichung Veterans General Hospital, Taichung 40705, Taiwan;
| | - I-Chieh Chen
- Department of Medical Research, Taichung Veterans General Hospital, Taichung 40705, Taiwan;
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27
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Wang X, Zhang Z, Ding Y, Chen T, Mucci L, Albanes D, Landi MT, Caporaso NE, Lam S, Tardon A, Chen C, Bojesen SE, Johansson M, Risch A, Bickeböller H, Wichmann HE, Rennert G, Arnold S, Brennan P, McKay JD, Field JK, Shete SS, Le Marchand L, Liu G, Andrew AS, Kiemeney LA, Zienolddiny-Narui S, Behndig A, Johansson M, Cox A, Lazarus P, Schabath MB, Aldrich MC, Hung RJ, Amos CI, Lin X, Christiani DC. Impact of individual level uncertainty of lung cancer polygenic risk score (PRS) on risk stratification. Genome Med 2024; 16:22. [PMID: 38317189 PMCID: PMC10840262 DOI: 10.1186/s13073-024-01298-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 01/26/2024] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND Although polygenic risk score (PRS) has emerged as a promising tool for predicting cancer risk from genome-wide association studies (GWAS), the individual-level accuracy of lung cancer PRS and the extent to which its impact on subsequent clinical applications remains largely unexplored. METHODS Lung cancer PRSs and confidence/credible interval (CI) were constructed using two statistical approaches for each individual: (1) the weighted sum of 16 GWAS-derived significant SNP loci and the CI through the bootstrapping method (PRS-16-CV) and (2) LDpred2 and the CI through posteriors sampling (PRS-Bayes), among 17,166 lung cancer cases and 12,894 controls with European ancestry from the International Lung Cancer Consortium. Individuals were classified into different genetic risk subgroups based on the relationship between their own PRS mean/PRS CI and the population level threshold. RESULTS Considerable variances in PRS point estimates at the individual level were observed for both methods, with an average standard deviation (s.d.) of 0.12 for PRS-16-CV and a much larger s.d. of 0.88 for PRS-Bayes. Using PRS-16-CV, only 25.0% of individuals with PRS point estimates in the lowest decile of PRS and 16.8% in the highest decile have their entire 95% CI fully contained in the lowest and highest decile, respectively, while PRS-Bayes was unable to find any eligible individuals. Only 19% of the individuals were concordantly identified as having high genetic risk (> 90th percentile) using the two PRS estimators. An increased relative risk of lung cancer comparing the highest PRS percentile to the lowest was observed when taking the CI into account (OR = 2.73, 95% CI: 2.12-3.50, P-value = 4.13 × 10-15) compared to using PRS-16-CV mean (OR = 2.23, 95% CI: 1.99-2.49, P-value = 5.70 × 10-46). Improved risk prediction performance with higher AUC was consistently observed in individuals identified by PRS-16-CV CI, and the best performance was achieved by incorporating age, gender, and detailed smoking pack-years (AUC: 0.73, 95% CI = 0.72-0.74). CONCLUSIONS Lung cancer PRS estimates using different methods have modest correlations at the individual level, highlighting the importance of considering individual-level uncertainty when evaluating the practical utility of PRS.
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Affiliation(s)
- Xinan Wang
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, 667 Huntington Ave, Boston, MA, 02115, USA
| | - Ziwei Zhang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Yi Ding
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, USA
| | - Tony Chen
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Lorelei Mucci
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Demetrios Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Neil E Caporaso
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephen Lam
- Department of Medicine, British Columbia Cancer Agency, University of British Columbia, Vancouver, Canada
| | - Adonina Tardon
- Faculty of Medicine, University of Oviedo and CIBERESP, Oviedo, Spain
| | - Chu Chen
- Department of Epidemiology, University of Washington School of Public Health, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Stig E Bojesen
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Copenhagen, Denmark
| | - Mattias Johansson
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Angela Risch
- Department of Biosciences and Medical Biology, Allergy-Cancer-BioNano Research Centre, University of Salzburg, and Cancer Cluster Salzburg, Salzburg, Austria
| | - Heike Bickeböller
- Department of Genetic Epidemiology, University Medical Center, Georg August University Göttingen, Göttingen, Germany
| | - H-Erich Wichmann
- Institute of Medical Informatics, Biometry and Epidemiology, Ludwig Maximilians University, Munich, Germany
| | - Gadi Rennert
- Clalit National Cancer Control Center, Carmel Medical Center and Technion Faculty of Medicine, Carmel, Haifa, Israel
| | - Susanne Arnold
- Markey Cancer Center, University of Kentucky, Lexington, KY, USA
| | - Paul Brennan
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - James D McKay
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - John K Field
- Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of Liverpool, Liverpool, UK
| | - Sanjay S Shete
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Geoffrey Liu
- Princess Margaret Cancer Centre, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Angeline S Andrew
- Department of Epidemiology, Department of Community and Family Medicine, Dartmouth Geisel School of Medicine, Hanover, NH, USA
| | - Lambertus A Kiemeney
- Department for Health Evidence, Department of Urology, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Annelie Behndig
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | | | - Angie Cox
- Department of Oncology and Metabolism, The Medical School, University of Sheffield, Sheffield, UK
| | - Philip Lazarus
- Department of Pharmaceutical Sciences, College of Pharmacy, Washington State University, Spokane, WA, USA
| | - Matthew B Schabath
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida, USA
| | - Melinda C Aldrich
- Department of Medicine, Department of Biomedical Informatics and Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Rayjean J Hung
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Christopher I Amos
- Institute for Clinical and Translational Research, Department of Medicine, Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - David C Christiani
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, 667 Huntington Ave, Boston, MA, 02115, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.
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Zhao Z, Gu S, Yang Y, Wu W, Du L, Wang G, Dong H. A cost-effectiveness analysis of lung cancer screening with low-dose computed tomography and a polygenic risk score. BMC Cancer 2024; 24:73. [PMID: 38218803 PMCID: PMC10787978 DOI: 10.1186/s12885-023-11800-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 12/26/2023] [Indexed: 01/15/2024] Open
Abstract
INTRODUCTION Several studies have proved that Polygenic Risk Score (PRS) is a potential candidate for realizing precision screening. The effectiveness of low-dose computed tomography (LDCT) screening for lung cancer has been proved to reduce lung cancer specific and overall mortality, but the cost-effectiveness of diverse screening strategies remained unclear. METHODS The comparative cost-effectiveness analysis used a Markov state-transition model to assess the potential effect and costs of the screening strategies incorporating PRS or not. A hypothetical cohort of 300,000 heavy smokers entered the study at age 50-74 years and were followed up until death or age 79 years. The model was run with a cycle length of 1 year. All the transition probabilities were validated and the performance value of PRS was extracted from published literature. A societal perspective was adopted and cost parameters were derived from databases of local medical insurance bureau. Sensitivity analyses and scenario analyses were conducted. RESULTS The strategy incorporating PRS was estimated to obtain an ICER of CNY 156,691.93 to CNY 221,741.84 per QALY gained compared with non-screening with the initial start age range across 50-74 years. The strategy that screened using LDCT alone from 70-74 years annually could obtain an ICER of CNY 80,880.85 per QALY gained, which was the most cost-effective strategy. The introduction of PRS as an extra eligible criteria was associated with making strategies cost-saving but also lose the capability of gaining more LYs compared with LDCT screening alone. CONCLUSION The PRS-based conjunctive screening strategy for lung cancer screening in China was not cost-effective using the willingness-to-pay threshold of 1 time Gross Domestic Product (GDP) per capita, and the optimal screening strategy for lung cancer still remains to be LDCT screening for now. Further optimization of the screening modality can be useful to consider adoption of PRS and prospective evaluation remains a research priority.
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Affiliation(s)
- Zixuan Zhao
- Department of Public Administration, School of Health Economics and Management, Nanjing University of Chinese Medicine, Nanjing, China
| | - Shuyan Gu
- Center for Health Policy and Management Studies, School of Government, Nanjing University, Nanjing, China
| | - Yi Yang
- Department of Science and Education of the Fourth Affiliated Hospital, and Center for Health Policy Studies, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Weijia Wu
- Department of Science and Education of the Fourth Affiliated Hospital, and Center for Health Policy Studies, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
| | - Lingbin Du
- Department of Cancer Prevention, Institute of Cancer and Basic Medicine, Chinese Academy of Sciences/Cancer Hospital of the University of Chinese Academy of Sciences/Zhejiang Cancer Hospital, Hangzhou, China
| | - Gaoling Wang
- Department of Public Administration, School of Health Economics and Management, Nanjing University of Chinese Medicine, Nanjing, China.
| | - Hengjin Dong
- Department of Science and Education of the Fourth Affiliated Hospital, and Center for Health Policy Studies, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
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29
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Giratallah H, Chenoweth MJ, Pouget JG, El-Boraie A, Alsaafin A, Lerman C, Knight J, Tyndale RF. CYP2A6 associates with respiratory disease risk and younger age of diagnosis: a phenome-wide association Mendelian Randomization study. Hum Mol Genet 2024; 33:198-210. [PMID: 37802914 PMCID: PMC10772040 DOI: 10.1093/hmg/ddad172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 09/21/2023] [Accepted: 10/02/2023] [Indexed: 10/08/2023] Open
Abstract
CYP2A6, a genetically variable enzyme, inactivates nicotine, activates carcinogens, and metabolizes many pharmaceuticals. Variation in CYP2A6 influences smoking behaviors and tobacco-related disease risk. This phenome-wide association study examined associations between a reconstructed version of our weighted genetic risk score (wGRS) for CYP2A6 activity with diseases in the UK Biobank (N = 395 887). Causal effects of phenotypic CYP2A6 activity (measured as the nicotine metabolite ratio: 3'-hydroxycotinine/cotinine) on the phenome-wide significant (PWS) signals were then estimated in two-sample Mendelian Randomization using the wGRS as the instrument. Time-to-diagnosis age was compared between faster versus slower CYP2A6 metabolizers for the PWS signals in survival analyses. In the total sample, six PWS signals were identified: two lung cancers and four obstructive respiratory diseases PheCodes, where faster CYP2A6 activity was associated with greater disease risk (Ps < 1 × 10-6). A significant CYP2A6-by-smoking status interaction was found (Psinteraction < 0.05); in current smokers, the same six PWS signals were found as identified in the total group, whereas no PWS signals were found in former or never smokers. In the total sample and current smokers, CYP2A6 activity causal estimates on the six PWS signals were significant in Mendelian Randomization (Ps < 5 × 10-5). Additionally, faster CYP2A6 metabolizer status was associated with younger age of disease diagnosis for the six PWS signals (Ps < 5 × 10-4, in current smokers). These findings support a role for faster CYP2A6 activity as a causal risk factor for lung cancers and obstructive respiratory diseases among current smokers, and a younger onset of these diseases. This research utilized the UK Biobank Resource.
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Affiliation(s)
- Haidy Giratallah
- Department of Pharmacology and Toxicology, University of Toronto, 1 King’s College Circle, Toronto, ON M5S 1A8, Canada
- Campbell Family Mental Health Research Institute, CAMH, 250 College St, Toronto, ON M5T 1R8, Canada
| | - Meghan J Chenoweth
- Department of Pharmacology and Toxicology, University of Toronto, 1 King’s College Circle, Toronto, ON M5S 1A8, Canada
- Campbell Family Mental Health Research Institute, CAMH, 250 College St, Toronto, ON M5T 1R8, Canada
- Department of Psychiatry, University of Toronto, 1 King’s College Circle, Toronto, ON M5S 1A8, Canada
| | - Jennie G Pouget
- Campbell Family Mental Health Research Institute, CAMH, 250 College St, Toronto, ON M5T 1R8, Canada
- Department of Psychiatry, University of Toronto, 1 King’s College Circle, Toronto, ON M5S 1A8, Canada
| | - Ahmed El-Boraie
- Department of Pharmacology and Toxicology, University of Toronto, 1 King’s College Circle, Toronto, ON M5S 1A8, Canada
- Campbell Family Mental Health Research Institute, CAMH, 250 College St, Toronto, ON M5T 1R8, Canada
| | - Alaa Alsaafin
- Department of Pharmacology and Toxicology, University of Toronto, 1 King’s College Circle, Toronto, ON M5S 1A8, Canada
- Campbell Family Mental Health Research Institute, CAMH, 250 College St, Toronto, ON M5T 1R8, Canada
| | - Caryn Lerman
- Norris Comprehensive Cancer Center, University of Southern California, 1441 Eastlake Ave, Los Angeles, CA 90033, United States
| | - Jo Knight
- Department of Psychiatry, University of Toronto, 1 King’s College Circle, Toronto, ON M5S 1A8, Canada
- Data Science Institute, Lancaster University Medical School, Lancaster LA1 4YE, United Kingdom
| | - Rachel F Tyndale
- Department of Pharmacology and Toxicology, University of Toronto, 1 King’s College Circle, Toronto, ON M5S 1A8, Canada
- Campbell Family Mental Health Research Institute, CAMH, 250 College St, Toronto, ON M5T 1R8, Canada
- Department of Psychiatry, University of Toronto, 1 King’s College Circle, Toronto, ON M5S 1A8, Canada
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30
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Zhang T, Xu X, Chang Q, Lv Y, Zhao Y, Niu K, Chen L, Xia Y. Ultraprocessed food consumption, genetic predisposition, and the risk of gout: the UK Biobank study. Rheumatology (Oxford) 2024; 63:165-173. [PMID: 37129545 DOI: 10.1093/rheumatology/kead196] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 04/04/2023] [Accepted: 04/13/2023] [Indexed: 05/03/2023] Open
Abstract
OBJECTIVE This study aimed to examine the interactions between ultraprocessed food (UPF) consumption and genetic predisposition with the risk of gout. METHODS This prospective cohort study analysed 181 559 individuals from the UK Biobank study who were free of gout at baseline. UPF was defined according to the NOVA classification. Assessment of genetic predisposition for gout was developed from a genetic risk score of 33 single nucleotide polymorphisms. Cox proportional hazards were used to estimate the associations between UPF consumption, genetic predisposition and the risk of gout. RESULTS Among the 181 559 individuals in the study, 1558 patients developed gout over 1 648 167 person-years of follow-up. In the multivariable adjustment model, compared with the lowest quartile of UPF consumption, the hazard ratio (HR) and 95% CI of the highest UPF consumption was 1.16 (1.01, 1.33) for gout risk, and there was a non-linear correlation between UPF consumption and the development of gout. In substitution analyses, replacing 20% of the weight of UPF in the daily intake with an equal amount of unprocessed or minimally processed food resulted in a 13% lower risk of gout (HR: 0.87; 95% CI: 0.79, 0.95). In the joint-effect analysis, the HR (95% CI) for gout was 1.90 (1.39, 2.60) in participants with high genetic predisposition and high UPF consumption, compared with those with low genetic predisposition and low UPF consumption. CONCLUSION In summary, UPF consumption was found to be associated with a higher risk of gout, particularly in those participants with genetic predisposition to gout. Our study indicated that reducing UPF consumption is crucial for gout prevention.
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Affiliation(s)
- Tingjing Zhang
- Department of Nutrition and Food Hygiene, School of Public Health, Wannan Medical College, Wuhu, China
| | - Xin Xu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shenyang, China
| | - Qing Chang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shenyang, China
| | - Yanling Lv
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuhong Zhao
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shenyang, China
| | - Kaijun Niu
- School of Public Health of Tianjin, University of Traditional Chinese Medicine, Tianjin, China
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China
| | - Liangkai Chen
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yang Xia
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China
- Liaoning Key Laboratory of Precision Medical Research on Major Chronic Disease, Shenyang, China
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Liu C, Hou J, Li W, Chen J, Li Y, Zhang J, Zhou W, Zhang W, Deng F, Wang Y, Chen L, Qin S, Meng X, Lu S. Construction and optimization of a polygenic risk model for venous thromboembolism in the Chinese population. J Vasc Surg Venous Lymphat Disord 2024; 12:101666. [PMID: 37619711 DOI: 10.1016/j.jvsv.2023.08.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 08/06/2023] [Accepted: 08/12/2023] [Indexed: 08/26/2023]
Abstract
BACKGROUND Venous thromboembolism (VTE) has both environmental and genetic risk factors. It is regulated by polygenes and multisites. The polygenic risk score (PRS) has been widely used because any single genetic biomarker failed to accurately predict the genetic risk of VTE. However, no polygenic risk model has been proposed for VTE in the Chinese population. Thus, we aimed to construct a PRS model for the first episode of VTE in the Chinese population. METHODS First, single nucleotide polymorphisms (SNPs) associated with VTE in genome-wide association studies, meta-analyses, and candidate gene studies were screened as variables for the PRS. The logarithm of the odds ratio was used to weight the variables. Second, a training set with simulated data from 1000 cases of VTE and 1000 controls was created with different genotypes and frequencies. Finally, we calculated the area under the receiver operating characteristic curve (AUC) to evaluate the discriminatory ability of the PRS model. RESULTS We screened 53 SNPs potentially associated with the first episode of VTE in the Chinese population. The AUC of the PRS-53 model (containing 53 SNPs) was 0.748 (95% confidence interval, 0.727-0.770) in the training set. From the largest weight to the smallest weight, SNPs were incrementally added to the model to calculate the AUC for model optimization. The AUC of the PRS-10 model (containing 10 SNPs) was 0.718 (95% confidence interval, 0.696-0.740), with no statistically significant difference from the AUC for the PRS-53 model. CONCLUSIONS The PRS-10 and PRS-53 models showed similar predictive abilities and satisfactory discriminatory power and can be used to predict the genetic risk of the first episode of VTE in the Chinese population. The simplified PRS-10 model is more efficient in clinical practice.
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Affiliation(s)
- Chao Liu
- Department of Vascular Surgery, The First Affiliated Hospital of Xi'an JiaoTong University, Xi'an, People's Republic of China
| | - Jiaxuan Hou
- Department of Vascular Surgery, The First Affiliated Hospital of Xi'an JiaoTong University, Xi'an, People's Republic of China
| | - Weiming Li
- Department of Vascular Surgery, The First Affiliated Hospital of Xi'an JiaoTong University, Xi'an, People's Republic of China
| | - Jinxing Chen
- Department of Vascular Surgery, The First Affiliated Hospital of Xi'an JiaoTong University, Xi'an, People's Republic of China
| | - Yane Li
- Department of Vascular Surgery, The First Affiliated Hospital of Xi'an JiaoTong University, Xi'an, People's Republic of China
| | - Jiawei Zhang
- Department of Vascular Surgery, The First Affiliated Hospital of Xi'an JiaoTong University, Xi'an, People's Republic of China
| | - Wei Zhou
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Wei Zhang
- Xi'an Agen Medicine Technology Co, Ltd, Xi'an, People's Republic of China
| | - Fenni Deng
- Xi'an Agen Medicine Technology Co, Ltd, Xi'an, People's Republic of China
| | - Yu Wang
- Xi'an Agen Medicine Technology Co, Ltd, Xi'an, People's Republic of China
| | - Luan Chen
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Shengying Qin
- Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Xiaohong Meng
- Xi'an Agen Medicine Technology Co, Ltd, Xi'an, People's Republic of China
| | - Shaoying Lu
- Department of Vascular Surgery, The First Affiliated Hospital of Xi'an JiaoTong University, Xi'an, People's Republic of China.
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32
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Zhu M, Lv J, Huang Y, Ma H, Li N, Wei X, Ji M, Ma Z, Song C, Wang C, Dai J, Tan F, Guo Y, Walters R, Millwood IY, Hung RJ, Christiani DC, Yu C, Jin G, Chen Z, Wei Q, Amos CI, Hu Z, Li L, Shen H. Ethnic differences of genetic risk and smoking in lung cancer: two prospective cohort studies. Int J Epidemiol 2023; 52:1815-1825. [PMID: 37676847 DOI: 10.1093/ije/dyad118] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 08/23/2023] [Indexed: 09/09/2023] Open
Abstract
BACKGROUND The role of genetic background underlying the disparity of relative risk of smoking and lung cancer between European populations and East Asians remains unclear. METHODS To assess the role of ethnic differences in genetic factors associated with smoking-related risk of lung cancer, we first constructed ethnic-specific polygenic risk scores (PRSs) to quantify individual genetic risk of lung cancer in Chinese and European populations. Then, we compared genetic risk and smoking as well as their interactions on lung cancer between two cohorts, including the China Kadoorie Biobank (CKB) and the UK Biobank (UKB). We also evaluated the absolute risk reduction over a 5-year period. RESULTS Differences in compositions and association effects were observed between the Chinese-specific PRSs and European-specific PRSs, especially for smoking-related loci. The PRSs were consistently associated with lung cancer risk, but stronger associations were observed in smokers of the UKB [hazard ratio (HR) 1.26 vs 1.15, P = 0.028]. A significant interaction between genetic risk and smoking on lung cancer was observed in the UKB (RERI, 11.39 (95% CI, 7.01-17.94)], but not in the CKB. Obvious higher absolute risk was observed in nonsmokers of the CKB, and a greater absolute risk reduction was found in the UKB (10.95 vs 7.12 per 1000 person-years, P <0.001) by comparing heavy smokers with nonsmokers, especially for those at high genetic risk. CONCLUSIONS Ethnic differences in genetic factors and the high incidence of lung cancer in nonsmokers of East Asian ethnicity were involved in the disparity of smoking-related risk of lung cancer.
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Affiliation(s)
- Meng Zhu
- Department of Epidemiology, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Yanqian Huang
- Department of Epidemiology, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Hongxia Ma
- Department of Epidemiology, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, China
| | - Ni Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Chinese Academy of Medical Sciences Key Laboratory for National Cancer Big Data Analysis and Implement, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaoxia Wei
- Department of Epidemiology, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Mengmeng Ji
- Department of Epidemiology, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Zhimin Ma
- Department of Epidemiology, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Ci Song
- Department of Epidemiology, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Cheng Wang
- Department of Epidemiology, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Juncheng Dai
- Department of Epidemiology, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Fengwei Tan
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yu Guo
- Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Robin Walters
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Iona Y Millwood
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Rayjean J Hung
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada
| | - David C Christiani
- Department of Environmental Health, Harvard School of Public Health, Department of Medicine, Harvard Medical School/Massachusetts General Hospital, Boston, USA
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Guangfu Jin
- Department of Epidemiology, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Qingyi Wei
- Duke Cancer Institute, Duke University Medical Center, Durham, USA
- Department of Population Health Sciences, Duke University School of Medicine, Durham, USA
| | - Christopher I Amos
- Baylor College of Medicine, Institute for Clinical and Translational Research, Houston, USA
| | - Zhibin Hu
- Department of Epidemiology, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- State Key Laboratory of Reproductive Medicine, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Hongbing Shen
- Department of Epidemiology, International Joint Research Center on Environment and Human Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, China
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Khan A, Shang N, Nestor JG, Weng C, Hripcsak G, Harris PC, Gharavi AG, Kiryluk K. Polygenic risk alters the penetrance of monogenic kidney disease. Nat Commun 2023; 14:8318. [PMID: 38097619 PMCID: PMC10721887 DOI: 10.1038/s41467-023-43878-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 11/22/2023] [Indexed: 12/17/2023] Open
Abstract
Chronic kidney disease (CKD) is determined by an interplay of monogenic, polygenic, and environmental risks. Autosomal dominant polycystic kidney disease (ADPKD) and COL4A-associated nephropathy (COL4A-AN) represent the most common forms of monogenic kidney diseases. These disorders have incomplete penetrance and variable expressivity, and we hypothesize that polygenic factors explain some of this variability. By combining SNP array, exome/genome sequence, and electronic health record data from the UK Biobank and All-of-Us cohorts, we demonstrate that the genome-wide polygenic score (GPS) significantly predicts CKD among ADPKD monogenic variant carriers. Compared to the middle tertile of the GPS for noncarriers, ADPKD variant carriers in the top tertile have a 54-fold increased risk of CKD, while ADPKD variant carriers in the bottom tertile have only a 3-fold increased risk of CKD. Similarly, the GPS significantly predicts CKD in COL4A-AN carriers. The carriers in the top tertile of the GPS have a 2.5-fold higher risk of CKD, while the risk for carriers in the bottom tertile is not different from the average population risk. These results suggest that accounting for polygenic risk improves risk stratification in monogenic kidney disease.
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Affiliation(s)
- Atlas Khan
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Ning Shang
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Jordan G Nestor
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Peter C Harris
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
| | - Ali G Gharavi
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA.
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Çalışkan M, Tazaki K. AI/ML advances in non-small cell lung cancer biomarker discovery. Front Oncol 2023; 13:1260374. [PMID: 38148837 PMCID: PMC10750392 DOI: 10.3389/fonc.2023.1260374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 11/16/2023] [Indexed: 12/28/2023] Open
Abstract
Lung cancer is the leading cause of cancer deaths among both men and women, representing approximately 25% of cancer fatalities each year. The treatment landscape for non-small cell lung cancer (NSCLC) is rapidly evolving due to the progress made in biomarker-driven targeted therapies. While advancements in targeted treatments have improved survival rates for NSCLC patients with actionable biomarkers, long-term survival remains low, with an overall 5-year relative survival rate below 20%. Artificial intelligence/machine learning (AI/ML) algorithms have shown promise in biomarker discovery, yet NSCLC-specific studies capturing the clinical challenges targeted and emerging patterns identified using AI/ML approaches are lacking. Here, we employed a text-mining approach and identified 215 studies that reported potential biomarkers of NSCLC using AI/ML algorithms. We catalogued these studies with respect to BEST (Biomarkers, EndpointS, and other Tools) biomarker sub-types and summarized emerging patterns and trends in AI/ML-driven NSCLC biomarker discovery. We anticipate that our comprehensive review will contribute to the current understanding of AI/ML advances in NSCLC biomarker research and provide an important catalogue that may facilitate clinical adoption of AI/ML-derived biomarkers.
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Affiliation(s)
- Minal Çalışkan
- Translational Science Department, Precision Medicine Function, Daiichi Sankyo, Inc., Basking Ridge, NJ, United States
| | - Koichi Tazaki
- Translational Science Department I, Precision Medicine Function, Daiichi Sankyo, Tokyo, Japan
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Wei B, Zhao J, Li J, Feng J, Sun M, Wang Z, Shi C, Yang K, Qin Y, Zhang J, Ma J, Dong H. Pathogenic germline variants in BRCA1 and TP53 increase lung cancer risk in Chinese. Cancer Med 2023; 12:21219-21228. [PMID: 37930190 PMCID: PMC10726856 DOI: 10.1002/cam4.6692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 10/07/2023] [Accepted: 10/27/2023] [Indexed: 11/07/2023] Open
Abstract
BACKGROUD Multiple studies have identified pathogenic germline variants in cancer susceptibility genes (CSGs) in Chinese lung cancer patients; however, accurate assessment of these variants' contributions to cancer predisposition is always hampered by the absence of data on the prevalence of these variants in the general population. It is necessary to conduct a large-scale case-control study to identify CSGs that significantly increase the risk of lung cancer. MATERIALS AND METHODS We performed targeted sequencing of a CSGs panel in 1117 lung cancer patients and 16,327 controls from the general Chinese population. RESULTS In comparison to controls, lung cancer patients had a considerably higher prevalence of pathogenic and likely pathogenic (P/LP) variations. Among lung cancer patients, 72% of P/LP variants carriers did not have a family cancer history, who would be ignored if germline testing was only provided for patients meeting family history-based criteria. Furthermore, compared to individuals with late-onset lung cancer, patients with early-onset lung cancer had a considerably higher prevalence of P/LP variations. With odds ratios (ORs) ranging from 4-fold (BRCA1: OR, 4.193; 95%CI, 1.382-10.768) to 29-fold (TP53: OR, 29.281; 95%CI, 1.523-1705.506), P/LP variants in the BRCA1 and TP53 genes were discovered to be strongly related to increased lung cancer risk. Additionally, with ORs ranging from 7.322-fold to infinity, we discovered 23 variations previously categorized as non-P/LP variants were highly enriched in lung cancer patients. CONCLUSION Our findings indicated that P/LP variants in BRCA1 and TP53 conferred increased risk of lung cancer in Chinese.
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Affiliation(s)
- Bing Wei
- Department of Molecular Pathology, Henan Key Laboratory of Molecular PathologyThe Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhouHenanChina
| | - Jiadong Zhao
- Nanjing Shenyou Institute of Genome ResearchNanjingJiangsuChina
| | - Jun Li
- Department of Molecular Pathology, Henan Key Laboratory of Molecular PathologyThe Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhouHenanChina
| | - Junnan Feng
- Department of Molecular Pathology, Henan Key Laboratory of Molecular PathologyThe Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhouHenanChina
| | - Manman Sun
- Nanjing Shenyou Institute of Genome ResearchNanjingJiangsuChina
| | - Zhizhong Wang
- Department of Molecular Pathology, Henan Key Laboratory of Molecular PathologyThe Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhouHenanChina
| | - Chao Shi
- Department of Molecular Pathology, Henan Key Laboratory of Molecular PathologyThe Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhouHenanChina
| | - Ke Yang
- Department of Molecular Pathology, Henan Key Laboratory of Molecular PathologyThe Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhouHenanChina
| | - Yue Qin
- Nanjing Shenyou Institute of Genome ResearchNanjingJiangsuChina
| | - Jing Zhang
- Nanjing Shenyou Institute of Genome ResearchNanjingJiangsuChina
| | - Jie Ma
- Department of Molecular Pathology, Henan Key Laboratory of Molecular PathologyThe Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer HospitalZhengzhouHenanChina
| | - Hui Dong
- Department of Gastroenterology, Shanghai Key Laboratory of Pancreatic DiseasesShanghai General Hospital, Shanghai Jiao Tong University School of MedicineShanghaiChina
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36
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Xia C, Basu P, Kramer BS, Li H, Qu C, Yu XQ, Canfell K, Qiao Y, Armstrong BK, Chen W. Cancer screening in China: a steep road from evidence to implementation. Lancet Public Health 2023; 8:e996-e1005. [PMID: 38000379 PMCID: PMC10665203 DOI: 10.1016/s2468-2667(23)00186-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 06/19/2023] [Accepted: 08/08/2023] [Indexed: 11/26/2023]
Abstract
Cancer screening has the potential to decrease mortality from several common cancer types. The first cancer screening programme in China was initiated in 1958 and the Cancer High Incidence Fields established in the 1970s have provided an extensive source of information for national cancer screening programmes. From 2012 onwards, four ongoing national cancer screening programmes have targeted eight cancer types: cervical, breast, colorectal, lung, oesophageal, stomach, liver, and nasopharyngeal cancers. By synthesising evidence from pilot screening programmes and population-based studies for various screening tests, China has developed a series of cancer screening guidelines. Nevertheless, challenges remain for the implementation of a fully successful population-based programme. The aim of this Review is to highlight the key milestones and the current status of cancer screening in China, describe what has been achieved to date, and identify the barriers in transitioning from evidence to implementation. We also make a set of implementation recommendations on the basis of the Chinese experience, which might be useful in the establishment of cancer screening programmes in other countries.
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Affiliation(s)
- Changfa Xia
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Partha Basu
- Early Detection, Prevention & Infections Branch, International Agency for Research on Cancer, Lyon, France
| | - Barnett S Kramer
- The Lisa Schwartz Foundation for Truth in Medicine, Hanover, NH, USA
| | - He Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chunfeng Qu
- State Key Lab of Molecular Oncology and Department of Immunology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xue Qin Yu
- The Daffodil Centre, The University of Sydney, A Joint Venture with Cancer Council NSW, Sydney, NSW, Australia
| | - Karen Canfell
- The Daffodil Centre, The University of Sydney, A Joint Venture with Cancer Council NSW, Sydney, NSW, Australia
| | - Youlin Qiao
- Department of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bruce K Armstrong
- School of Public Health, University of Sydney, Sydney, NSW, Australia; School of Global and Population Health, University of Western Australia, Perth, WA, Australia
| | - Wanqing Chen
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
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Khanna NN, Singh M, Maindarkar M, Kumar A, Johri AM, Mentella L, Laird JR, Paraskevas KI, Ruzsa Z, Singh N, Kalra MK, Fernandes JFE, Chaturvedi S, Nicolaides A, Rathore V, Singh I, Teji JS, Al-Maini M, Isenovic ER, Viswanathan V, Khanna P, Fouda MM, Saba L, Suri JS. Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review. J Korean Med Sci 2023; 38:e395. [PMID: 38013648 PMCID: PMC10681845 DOI: 10.3346/jkms.2023.38.e395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 10/15/2023] [Indexed: 11/29/2023] Open
Abstract
Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.
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Affiliation(s)
- Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
- Asia Pacific Vascular Society, New Delhi, India
| | - Manasvi Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- Bennett University, Greater Noida, India
| | - Mahesh Maindarkar
- Asia Pacific Vascular Society, New Delhi, India
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- School of Bioengineering Sciences and Research, Maharashtra Institute of Technology's Art, Design and Technology University, Pune, India
| | | | - Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, Canada
| | - Laura Mentella
- Department of Medicine, Division of Cardiology, University of Toronto, Toronto, Canada
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA, USA
| | | | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, Szeged, Hungary
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India
| | | | | | - Seemant Chaturvedi
- Department of Neurology & Stroke Program, University of Maryland, Baltimore, MD, USA
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Cyprus
| | - Vijay Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA, USA
| | - Inder Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | - Jagjit S Teji
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Mostafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON, Canada
| | - Esma R Isenovic
- Department of Radiobiology and Molecular Genetics, National Institute of The Republic of Serbia, University of Belgrade, Beograd, Serbia
| | | | - Puneet Khanna
- Department of Anaesthesiology, AIIMS, New Delhi, India
| | - Mostafa M Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID, USA
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria, Cagliari, Italy
| | - Jasjit S Suri
- Asia Pacific Vascular Society, New Delhi, India
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
- Department of Computer Engineering, Graphic Era Deemed to be University, Dehradun, India.
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Trendowski MR, Lusk CM, Wenzlaff AS, Neslund-Dudas C, Gadgeel SM, Soubani AO, Schwartz AG. Assessing a Polygenic Risk Score for Lung Cancer Susceptibility in Non-Hispanic White and Black Populations. Cancer Epidemiol Biomarkers Prev 2023; 32:1558-1563. [PMID: 37578347 PMCID: PMC10841320 DOI: 10.1158/1055-9965.epi-23-0174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 06/14/2023] [Accepted: 08/10/2023] [Indexed: 08/15/2023] Open
Abstract
BACKGROUND Polygenic risk scores (PRS) have become an increasingly popular approach to evaluate cancer susceptibility, but have not adequately represented Black populations in model development. METHODS We used a previously published lung cancer PRS on the basis of 80 SNPs associated with lung cancer risk in the OncoArray cohort and validated in UK Biobank. The PRS was evaluated for association with lung cancer risk adjusting for age, sex, total pack-years, family history of lung cancer, history of chronic obstructive pulmonary disease, and the top five principal components for genetic ancestry. RESULTS Among the 80 PRS SNPs included in the score, 14 were significantly associated with lung cancer risk (P < 0.05) in INHALE White participants, while there were no significant SNPs among INHALE Black participants. After adjusting for covariates, the PRS was significantly associated with risk in Whites (continuous score P = 0.007), but not in Blacks (continuous score P = 0.88). The PRS remained a statistically significant predictor of lung cancer risk in Whites ineligible for lung cancer screening under current U.S. Preventive Services Task Force guidelines (P = 0.02). CONCLUSIONS Using a previously validated PRS, we did find some predictive ability for lung cancer in INHALE White participants beyond traditional risk factors. However, this effect was not observed in Black participants, indicating the need to develop and validate ancestry-specific lung cancer risk models. IMPACT While a previously published lung cancer PRS was able to stratify White participants into different levels of risk, the model was not predictive in Blacks. Our findings highlight the need to develop and validate ancestry-specific lung cancer risk models.
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Affiliation(s)
- Matthew R. Trendowski
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI, USA
- Karmanos Cancer Institute, Detroit, MI, USA
| | - Christine M. Lusk
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI, USA
- Karmanos Cancer Institute, Detroit, MI, USA
| | - Angela S. Wenzlaff
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI, USA
- Karmanos Cancer Institute, Detroit, MI, USA
| | - Christine Neslund-Dudas
- Department of Public Health Sciences, Henry Ford Health, Detroit, MI, USA
- Henry Ford Cancer Institute, Henry Ford Health, Detroit, MI, USA
| | | | - Ayman O. Soubani
- Karmanos Cancer Institute, Detroit, MI, USA
- Division of Pulmonary, Critical Care and Sleep Medicine, Wayne State University School of Medicine, Detroit, MI, USA
| | - Ann G. Schwartz
- Department of Oncology, Wayne State University School of Medicine, Detroit, MI, USA
- Karmanos Cancer Institute, Detroit, MI, USA
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Huang Y, Bao T, Zhang T, Ji G, Wang Y, Ling Z, Li W. Machine Learning Study of SNPs in Noncoding Regions to Predict Non-small Cell Lung Cancer Susceptibility. Clin Oncol (R Coll Radiol) 2023; 35:701-712. [PMID: 37689528 DOI: 10.1016/j.clon.2023.08.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 07/23/2023] [Accepted: 08/30/2023] [Indexed: 09/11/2023]
Abstract
Non-small cell lung cancer (NSCLC) is the most common pathological subtype of lung cancer. Both environmental and genetic factors have been reported to impact the lung cancer susceptibility. We conducted a genome-wide association study (GWAS) of 287 NSCLC patients and 467 healthy controls in a Chinese population using the Illumina Genome-Wide Asian Screening Array Chip on 712,095 SNPs (single nucleotide polymorphisms). Using logistic regression modeling, GWAS identified 17 new noncoding region SNP loci associated with the NSCLC risk, and the top three (rs80040741, rs9568547, rs6010259) were under a stringent p-value (<3.02e-6). Notably, rs80040741 and rs6010259 were annotated from the intron regions of MUC3A and MLC1, respectively. Together with another five SNPs previously reported in Chinese NSCLC patients and another four covariates (e.g., smoking status, age, low dose CT screening, sex), a predictive model by machine learning methods can separate the NSCLC from healthy controls with an accuracy of 86%. This is the first time to apply machine learning method in predicting the NSCLC susceptibility using both genetic and clinical characteristics. Our findings will provide a promising method in NSCLC early diagnosis and improve our understanding of applying machine learning methods in precision medicine.
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Affiliation(s)
- Y Huang
- Health Management Center, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China; Institute of Respiratory Healthy, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - T Bao
- Health Management Center, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China; Institute of Respiratory Healthy, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - T Zhang
- Health Management Center, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China; Institute of Respiratory Healthy, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - G Ji
- Health Management Center, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China; Institute of Respiratory Healthy, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Y Wang
- Health Management Center, General Practice Medical Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China; Institute of Respiratory Healthy, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Z Ling
- Chengdu Genepre Technology Co., LTD, Chengdu, Sichuan, China
| | - W Li
- Institute of Respiratory Healthy, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China; Department of Respiratory and Critical Care Medicine, Institute of Respiratory Healthy, Precision Medicine Key Laboratory of Sichuan Province, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China; Precision Medicine Center, Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China; The Research Units of West China, Chinese Academy of Medical Sciences, West China Hospital, Chengdu, Sichuan 610041, China; State Key Laboratory of Respiratory Health and Multimorbidity, Chengdu, Sichuan 610041, West China Hospital, China.
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40
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Li Z, Lu L, Deng Y, Zhuo A, Hu F, Sun W, Huang G, Liu L, Rao B, Lu J, Yang L. Genetic susceptibility loci of lung cancer are associated with malignant risk of pulmonary nodules and improve malignancy diagnosis based on CEA levels. Chin J Cancer Res 2023; 35:501-510. [PMID: 37969964 PMCID: PMC10643346 DOI: 10.21147/j.issn.1000-9604.2023.05.07] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 10/20/2023] [Indexed: 11/17/2023] Open
Abstract
Objective The heightened prevalence of pulmonary nodules (PN) has escalated its significance as a public health concern. While the precise identification of high-risk PN carriers for malignancy remains an ongoing challenge, genetic variants hold potentials as determinants of disease susceptibility that can aid in diagnosis. Yet, current understanding of the genetic loci associated with malignant PN (MPN) risk is limited. Methods A frequency-matched case-control study was performed, comprising 247 MPN cases and 412 benign NP (BNP) controls. We genotyped 11 established susceptibility loci for lung cancer in a Chinese cohort. Loci associated with MPN risk were utilized to compute a polygenic risk score (PRS). This PRS was subsequently incorporated into the diagnostic evaluation of MPNs, with emphasis on serum tumor biomarkers. Results Loci rs10429489G>A, rs17038564A>G, and rs12265047A>G were identified as being associated with an increased risk of MPNs. The PRS, formulated from the cumulative risk effects of these loci, correlated with the malignant risk of PNs in a dose-dependent fashion. A high PRS was found to amplify the MPN risk by 156% in comparison to a low PRS [odds ratio (OR)=2.56, 95% confidence interval (95% CI), 1.40-4.67]. Notably, the PRS was observed to enhance the diagnostic accuracy of serum carcinoembryonic antigen (CEA) in distinguishing MPNs from BPNs, with diagnostic values rising from 0.716 to 0.861 across low- to high-PRS categories. Further bioinformatics investigations pinpointed rs10429489G>A as an expression quantitative trait locus. Conclusions Loci rs10429489G>A, rs17038564A>G, and rs12265047A>G contribute to MPN risk and augment the diagnostic precision for MPNs based on serum CEA concentrations.
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Affiliation(s)
- Zhi Li
- The State Key Lab of Respiratory Disease, Institute of Public Health, Guangzhou Medical University, Guangzhou 511436, China
| | - Liming Lu
- The State Key Lab of Respiratory Disease, Institute of Public Health, Guangzhou Medical University, Guangzhou 511436, China
| | - Yibin Deng
- Center for Medical Laboratory Science, the Affiliated Hospital of Youjiang Medical University for Nationalities, Baise 533000, China
| | - Amei Zhuo
- The State Key Lab of Respiratory Disease, Institute of Public Health, Guangzhou Medical University, Guangzhou 511436, China
| | - Fengling Hu
- The State Key Lab of Respiratory Disease, Institute of Public Health, Guangzhou Medical University, Guangzhou 511436, China
| | - Wanwen Sun
- The State Key Lab of Respiratory Disease, Institute of Public Health, Guangzhou Medical University, Guangzhou 511436, China
| | - Guitian Huang
- Physical examination center, Guangzhou First People’s Hospital, Guangzhou 511468, China
| | - Linyuan Liu
- The State Key Lab of Respiratory Disease, Institute of Public Health, Guangzhou Medical University, Guangzhou 511436, China
| | - Boqi Rao
- The State Key Lab of Respiratory Disease, Institute of Public Health, Guangzhou Medical University, Guangzhou 511436, China
| | - Jiachun Lu
- The State Key Lab of Respiratory Disease, Institute of Public Health, Guangzhou Medical University, Guangzhou 511436, China
| | - Lei Yang
- The State Key Lab of Respiratory Disease, Institute of Public Health, Guangzhou Medical University, Guangzhou 511436, China
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Long E, Yin J, Shin JH, Li Y, Kane A, Patel H, Luong T, Xia J, Han Y, Byun J, Zhang T, Zhao W, Landi MT, Rothman N, Lan Q, Chang YS, Yu F, Amos C, Shi J, Lee JG, Kim EY, Choi J. Context-aware single-cell multiome approach identified cell-type specific lung cancer susceptibility genes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.25.559336. [PMID: 37808664 PMCID: PMC10557605 DOI: 10.1101/2023.09.25.559336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Genome-wide association studies (GWAS) identified over fifty loci associated with lung cancer risk. However, the genetic mechanisms and target genes underlying these loci are largely unknown, as most risk-associated-variants might regulate gene expression in a context-specific manner. Here, we generated a barcode-shared transcriptome and chromatin accessibility map of 117,911 human lung cells from age/sex-matched ever- and never-smokers to profile context-specific gene regulation. Accessible chromatin peak detection identified cell-type-specific candidate cis-regulatory elements (cCREs) from each lung cell type. Colocalization of lung cancer candidate causal variants (CCVs) with these cCREs prioritized the variants for 68% of the GWAS loci, a subset of which was also supported by transcription factor abundance and footprinting. cCRE colocalization and single-cell based trait relevance score nominated epithelial and immune cells as the main cell groups contributing to lung cancer susceptibility. Notably, cCREs of rare proliferating epithelial cell types, such as AT2-proliferating (0.13%) and basal cells (1.8%), overlapped with CCVs, including those in TERT. A multi-level cCRE-gene linking system identified candidate susceptibility genes from 57% of lung cancer loci, including those not detected in tissue- or cell-line-based approaches. cCRE-gene linkage uncovered that adjacent genes expressed in different cell types are correlated with distinct subsets of coinherited CCVs, including JAML and MPZL3 at the 11q23.3 locus. Our data revealed the cell types and contexts where the lung cancer susceptibility genes are functional.
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Affiliation(s)
- Erping Long
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Current affiliation: Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jinhu Yin
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Ju Hye Shin
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Yuyan Li
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Alexander Kane
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Harsh Patel
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Thong Luong
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jun Xia
- Department of Biomedical Sciences, Creighton University, Omaha, NE, USA
| | - Younghun Han
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Jinyoung Byun
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Tongwu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Wei Zhao
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Nathaniel Rothman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Qing Lan
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Yoon Soo Chang
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Fulong Yu
- Guangzhou National Laboratory, Guangzhou International Bio Island, Guangzhou, China
| | - Christopher Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Jianxin Shi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jin Gu Lee
- Department of Thoracic and Cardiovascular Surgery, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Eun Young Kim
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jiyeon Choi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
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Cheng X, Du F, Long X, Huang J. Genetic Inheritance Models of Non-Syndromic Cleft Lip with or without Palate: From Monogenic to Polygenic. Genes (Basel) 2023; 14:1859. [PMID: 37895208 PMCID: PMC10606748 DOI: 10.3390/genes14101859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/18/2023] [Accepted: 09/21/2023] [Indexed: 10/29/2023] Open
Abstract
Non-syndromic cleft lip with or without palate (NSCL/P) is a prevalent birth defect that affects 1/500-1/1400 live births globally. The genetic basis of NSCL/P is intricate and involves both genetic and environmental factors. In the past few years, various genetic inheritance models have been proposed to elucidate the underlying mechanisms of NSCL/P. These models range from simple monogenic inheritance to more complex polygenic inheritance. Here, we present a comprehensive overview of the genetic inheritance model of NSCL/P exemplified by representative genes and regions from both monogenic and polygenic perspectives. We also summarize existing association studies and corresponding loci of NSCL/P within the Chinese population and highlight the potential of utilizing polygenic risk scores for risk stratification of NSCL/P. The potential application of polygenic models offers promising avenues for improved risk assessment and personalized approaches in the prevention and management of NSCL/P individuals.
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Affiliation(s)
- Xi Cheng
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China; (X.C.); (F.D.); (X.L.)
| | - Fengzhou Du
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China; (X.C.); (F.D.); (X.L.)
- Department of Plastic Surgery, Peking Union Medical College Hospital, Beijing 100730, China
| | - Xiao Long
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China; (X.C.); (F.D.); (X.L.)
- Department of Plastic Surgery, Peking Union Medical College Hospital, Beijing 100730, China
| | - Jiuzuo Huang
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China; (X.C.); (F.D.); (X.L.)
- Department of Plastic Surgery, Peking Union Medical College Hospital, Beijing 100730, China
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Wang Y, Ding Y, Liu S, Wang C, Zhang E, Chen C, Zhu M, Zhang J, Zhu C, Ji M, Dai J, Jin G, Hu Z, Shen H, Ma H. Integrative splicing-quantitative-trait-locus analysis reveals risk loci for non-small-cell lung cancer. Am J Hum Genet 2023; 110:1574-1589. [PMID: 37562399 PMCID: PMC10502736 DOI: 10.1016/j.ajhg.2023.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 07/13/2023] [Accepted: 07/14/2023] [Indexed: 08/12/2023] Open
Abstract
Splicing quantitative trait loci (sQTLs) have been demonstrated to contribute to disease etiology by affecting alternative splicing. However, the role of sQTLs in the development of non-small-cell lung cancer (NSCLC) remains unknown. Thus, we performed a genome-wide sQTL study to identify genetic variants that affect alternative splicing in lung tissues from 116 individuals of Chinese ancestry, which resulted in the identification of 1,385 sQTL-harboring genes (sGenes) containing 378,210 significant variant-intron pairs. A comprehensive characterization of these sQTLs showed that they were enriched in actively transcribed regions, genetic regulatory elements, and splicing-factor-binding sites. Moreover, sQTLs were largely distinct from expression quantitative trait loci (eQTLs) and showed significant enrichment in potential risk loci of NSCLC. We also integrated sQTLs into NSCLC GWAS datasets (13,327 affected individuals and 13,328 control individuals) by using splice-transcriptome-wide association study (spTWAS) and identified alternative splicing events in 19 genes that were significantly associated with NSCLC risk. By using functional annotation and experiments, we confirmed an sQTL variant, rs35861926, that reduced the risk of lung adenocarcinoma (rs35861926-T, OR = 0.88, 95% confidence interval [CI]: 0.82-0.93, p = 1.87 × 10-5) by promoting FARP1 exon 20 skipping to downregulate the expression level of the long transcript FARP1-011. Transcript FARP1-011 promoted the migration and proliferation of lung adenocarcinoma cells. Overall, our study provided informative lung sQTL resources and insights into the molecular mechanisms linking sQTL variants to NSCLC risk.
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Affiliation(s)
- Yuzhuo Wang
- Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Yue Ding
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Su Liu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Cheng Wang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Erbao Zhang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Congcong Chen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Meng Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Jing Zhang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Chen Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Department of Cancer Prevention, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang 310022, China
| | - Mengmeng Ji
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Juncheng Dai
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Guangfu Jin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Zhibin Hu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Hongbing Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing 100730, China.
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
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Fan J, Hong T, Zhao X, Liang S, Zhu M, Jiang Y, Jin G, Hu Z, Ma H, Dai J, Shen H. A two-stage genome-wide association study identified four potential early-onset nonsmall cell lung cancer risk loci based on 26,652 participants in Chinese population. Mol Carcinog 2023; 62:1263-1270. [PMID: 37232355 DOI: 10.1002/mc.23561] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 02/28/2023] [Accepted: 05/06/2023] [Indexed: 05/27/2023]
Abstract
Early-onset lung cancer is rare with an increasing incidence rate. Although several genetic variants have been identified for it with candidate gene approaches, no genome-wide association study (GWAS) has been reported. In this study, a two-stage strategy was adopted: firstly we performed a GWAS to identify variants associated with early-onset nonsmall-cell lung cancer (NSCLC) risk using 2556 cases (age ≤ 50 years) and 13,327 controls by logistic regression model. To further discriminate younger cases from older ones, we took a case-case analysis for the promising variants with above early-onset cases and 10,769 cases (age > 50 years) by Cox regression model. After combining these results, we identified four early-onset NSCLC susceptibility loci at 5p15.33 (rs2853677, odds ratio [OR] = 1.48, 95% confidence interval [CI]: 1.36-1.60, Pcase-control = 3.58 × 10-21 ; hazard ratio [HR] = 1.10, 95% CI: 1.04-1.16, Pcase-case = 6.77 × 10-4 ), 5p15.1 (rs2055817, OR = 1.24, 95% CI: 1.15-1.35, Pcase-control = 1.39 × 10-7 ; HR = 1.08, 95% CI: 1.02-1.14, Pcase-case = 6.90 × 10-3 ), 6q24.2 (rs9403497, OR = 1.24, 95% CI: 1.15-1.35, Pcase-control = 1.61 × 10-7 ; HR = 1.11, 95% CI: 1.05-1.17, Pcase-case = 3.60 × 10-4 ) and 12q14.3 (rs4762093, OR = 1.31, 95% CI: 1.18-1.45, Pcase-control = 1.90 × 10-7 ; HR = 1.10, 95% CI: 1.03-1.18, Pcase-case = 7.49 × 10-3 ). Except for 5p15.33, other loci were found to be associated with NSCLC risk for the first time. All of them had stronger effects in younger patients than in older ones. These results provide a promising overview for early-onset NSCLC genetics.
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Affiliation(s)
- Jingyi Fan
- Department of Epidemiology, Center for Global Health, School of Public Health, Gusu School, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
- Health Management Center, Gusu School, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, China
| | - Tongtong Hong
- Department of Epidemiology, Center for Global Health, School of Public Health, Gusu School, Nanjing Medical University, Nanjing, China
- Institute of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China
| | - Xiaoyu Zhao
- Department of Epidemiology, Center for Global Health, School of Public Health, Gusu School, Nanjing Medical University, Nanjing, China
| | - Shuang Liang
- Department of Epidemiology, Center for Global Health, School of Public Health, Gusu School, Nanjing Medical University, Nanjing, China
| | - Meng Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Gusu School, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Yue Jiang
- Department of Epidemiology, Center for Global Health, School of Public Health, Gusu School, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Guangfu Jin
- Department of Epidemiology, Center for Global Health, School of Public Health, Gusu School, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Zhibin Hu
- Department of Epidemiology, Center for Global Health, School of Public Health, Gusu School, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Gusu School, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Juncheng Dai
- Department of Epidemiology, Center for Global Health, School of Public Health, Gusu School, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Hongbing Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Gusu School, Nanjing Medical University, Nanjing, China
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
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Leiter A, Veluswamy RR, Wisnivesky JP. The global burden of lung cancer: current status and future trends. Nat Rev Clin Oncol 2023; 20:624-639. [PMID: 37479810 DOI: 10.1038/s41571-023-00798-3] [Citation(s) in RCA: 150] [Impact Index Per Article: 150.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/20/2023] [Indexed: 07/23/2023]
Abstract
Lung cancer is the leading cause of cancer-related death worldwide. However, lung cancer incidence and mortality rates differ substantially across the world, reflecting varying patterns of tobacco smoking, exposure to environmental risk factors and genetics. Tobacco smoking is the leading risk factor for lung cancer. Lung cancer incidence largely reflects trends in smoking patterns, which generally vary by sex and economic development. For this reason, tobacco control campaigns are a central part of global strategies designed to reduce lung cancer mortality. Environmental and occupational lung cancer risk factors, such as unprocessed biomass fuels, asbestos, arsenic and radon, can also contribute to lung cancer incidence in certain parts of the world. Over the past decade, large-cohort clinical studies have established that low-dose CT screening reduces lung cancer mortality, largely owing to increased diagnosis and treatment at earlier disease stages. These data have led to recommendations that individuals with a high risk of lung cancer undergo screening in several economically developed countries and increased implementation of screening worldwide. In this Review, we provide an overview of the global epidemiology of lung cancer. Lung cancer risk factors and global risk reduction efforts are also discussed. Finally, we summarize lung cancer screening policies and their implementation worldwide.
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Affiliation(s)
- Amanda Leiter
- Division of Endocrinology, Diabetes, and Bone Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Rajwanth R Veluswamy
- Division of Hematology and Oncology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Juan P Wisnivesky
- Division of General Internal Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Chen LS, Baker TB, Ramsey A, Amos CI, Bierut LJ. Genomic medicine to reduce tobacco and related disorders: Translation to precision prevention and treatment. ADDICTION NEUROSCIENCE 2023; 7:100083. [PMID: 37602286 PMCID: PMC10434839 DOI: 10.1016/j.addicn.2023.100083] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/22/2023]
Abstract
Genomic medicine can enhance prevention and treatment. First, we propose that advances in genomics have the potential to enhance assessment of disease risk, improve prognostic predictions, and guide treatment development and application. Clinical implementation of polygenic risk scores (PRSs) has emerged as an area of active research. The pathway from genomic discovery to implementation is an iterative process. Second, we provide examples on how genomic medicine has the potential to solve problems in prevention and treatment using two examples: Lung cancer screening and evidence-based tobacco treatment are both under-utilized and great opportunities for genomic interventions. Third, we discuss the translational process for developing genomic interventions from evidence to implementation by presenting a model to evaluate genomic evidence for clinical implementation, mechanisms of genomic interventions, and patient desire for genomic interventions. Fourth, we present potential challenges in genomic interventions including a great need for evidence in all diverse populations, little evidence on treatment algorithms, challenges in accommodating a dynamic evidence base, and implementation challenges in real world clinical settings. Finally, we conclude that research to identify genomic markers that are associated with smoking cessation success and the efficacy of smoking cessation treatments is needed to empower people of all diverse ancestry. Importantly, genomic data can be used to help identify patients with elevated risk for nicotine addiction, difficulty quitting smoking, favorable response to specific pharmacotherapy, and tobacco-related health problems.
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Affiliation(s)
- Li-Shiun Chen
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
- Alvin J. Siteman Cancer Center at Barnes-Jewish Hospital, Washington University School of Medicine, St. Louis, MO, United States
| | - Timothy B. Baker
- Center for Tobacco Research and Intervention, School of Medicine and Public Health, University of Wisconsin, Madison, WI, United States
| | - Alex Ramsey
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
- Alvin J. Siteman Cancer Center at Barnes-Jewish Hospital, Washington University School of Medicine, St. Louis, MO, United States
| | - Christopher I. Amos
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth College, Hanover, NH, United States
- Department of Medicine, Baylor College of Medicine, Institute for Clinical and Translational Research, Houston, TX, United States
| | - Laura J. Bierut
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
- Alvin J. Siteman Cancer Center at Barnes-Jewish Hospital, Washington University School of Medicine, St. Louis, MO, United States
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Feng X. Integrative analysis of GWAS and transcriptomics data reveal key genes for non-small lung cancer. Med Oncol 2023; 40:270. [PMID: 37592093 DOI: 10.1007/s12032-023-02139-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 07/25/2023] [Indexed: 08/19/2023]
Abstract
Lung cancer is one of the world's most common and deadly cancers. The two main types of lung cancer are non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). More than 85% of lung cancers are NSCLC. Genetic factors play a significant role in the risk of NSCLC. Growing studies focus on studying risk factors at the molecular level. The aim of the study is to build a pipeline to integrate Genome-wide association analysis (GWAS) and transcriptomics data with machine learning to effectively identify genetic risk factors of NSCLC. GWAS datasets and GWAS summary data were downloaded from GWAS catalog, which include lung carcinoma genetic variants among the European population. Then, with the GWAS summary, data functional analysis of significant SNPs was performed using a webserver called FUMAGWAS. The transcriptomics data of NSCLC and non-NSCLC people were used to build a machine learning model to identify the key genes that help predict the NSCLC. The top up-regulation and down-regulation genes were identified by the BART cancer webserver, and the mechanistic roles of the genes were validated by literature review. By performing integrative analysis of GWAS and transcriptomics analysis using machine learning, we identified multiple SNPs and genes that related to NSCLC. The computational pipeline may facilitate the biomarker discovery for NSCLC and other diseases.
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Affiliation(s)
- Xiangxiong Feng
- University of California Davis, Shields Avenue, Davis, CA, 95616, USA.
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Walters RG, Millwood IY, Lin K, Schmidt Valle D, McDonnell P, Hacker A, Avery D, Edris A, Fry H, Cai N, Kretzschmar WW, Ansari MA, Lyons PA, Collins R, Donnelly P, Hill M, Peto R, Shen H, Jin X, Nie C, Xu X, Guo Y, Yu C, Lv J, Clarke RJ, Li L, Chen Z. Genotyping and population characteristics of the China Kadoorie Biobank. CELL GENOMICS 2023; 3:100361. [PMID: 37601966 PMCID: PMC10435379 DOI: 10.1016/j.xgen.2023.100361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 02/09/2023] [Accepted: 06/24/2023] [Indexed: 08/22/2023]
Abstract
The China Kadoorie Biobank (CKB) is a population-based prospective cohort of >512,000 adults recruited from 2004 to 2008 from 10 geographically diverse regions across China. Detailed data from questionnaires and physical measurements were collected at baseline, with additional measurements at three resurveys involving ∼5% of surviving participants. Analyses of genome-wide genotyping, for >100,000 participants using custom-designed Axiom arrays, reveal extensive relatedness, recent consanguinity, and signatures reflecting large-scale population movements from recent Chinese history. Systematic genome-wide association studies of incident disease, captured through electronic linkage to death and disease registries and to the national health insurance system, replicate established disease loci and identify 14 novel disease associations. Together with studies of candidate drug targets and disease risk factors and contributions to international genetics consortia, these demonstrate the breadth, depth, and quality of the CKB data. Ongoing high-throughput omics assays of collected biosamples and planned whole-genome sequencing will further enhance the scientific value of this biobank.
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Affiliation(s)
- Robin G. Walters
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
- MRC Population Health Research Unit, University of Oxford, Oxford OX3 7LF, UK
| | - Iona Y. Millwood
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
- MRC Population Health Research Unit, University of Oxford, Oxford OX3 7LF, UK
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Dan Schmidt Valle
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Pandora McDonnell
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Alex Hacker
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Daniel Avery
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Ahmed Edris
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Hannah Fry
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Na Cai
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | | | - M. Azim Ansari
- Nuffield Department of Medicine, Oxford University, Oxford OX1 3SY, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Paul A. Lyons
- Cambridge Institute for Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge CB2 0AW, UK
- Department of Medicine, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Rory Collins
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Peter Donnelly
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Michael Hill
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
- MRC Population Health Research Unit, University of Oxford, Oxford OX3 7LF, UK
| | - Richard Peto
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Hongbing Shen
- Department of Epidemiology, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing 211116, China
| | - Xin Jin
- BGI-Shenzhen, Shenzhen 518083, China
| | - Chao Nie
- BGI-Shenzhen, Shenzhen 518083, China
| | - Xun Xu
- BGI-Shenzhen, Shenzhen 518083, China
| | - Yu Guo
- Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing 100191, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing 100191, China
| | - Robert J. Clarke
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing 100191, China
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
- MRC Population Health Research Unit, University of Oxford, Oxford OX3 7LF, UK
| | - China Kadoorie Biobank Collaborative Group
- Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
- MRC Population Health Research Unit, University of Oxford, Oxford OX3 7LF, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- Nuffield Department of Medicine, Oxford University, Oxford OX1 3SY, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK
- Cambridge Institute for Therapeutic Immunology and Infectious Disease, University of Cambridge, Cambridge CB2 0AW, UK
- Department of Medicine, University of Cambridge, Cambridge CB2 0QQ, UK
- Department of Epidemiology, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing 211116, China
- BGI-Shenzhen, Shenzhen 518083, China
- Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing 100037, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Center for Public Health and Epidemic Preparedness and Response, Peking University, Beijing 100191, China
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He H, Shen Q, He MM, Qiu W, Wang H, Zhang S, Qin S, Lu Z, Zhu Y, Tian J, Chang J, Wang K, Zhang X, Miao X, Song M, Zhong R. In Utero and Childhood/Adolescence Exposure to Tobacco Smoke, Genetic Risk, and Cancer Incidence in Adulthood: A Prospective Cohort Study. Mayo Clin Proc 2023; 98:1164-1176. [PMID: 37422733 DOI: 10.1016/j.mayocp.2023.03.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 03/11/2023] [Accepted: 03/28/2023] [Indexed: 07/10/2023]
Abstract
OBJECTIVE To evaluate the association of early-life tobacco smoke exposure, especially interacting with cancer genetic variants, with adult cancer. PARTICIPANTS AND METHODS We examined the associations of in utero tobacco smoke exposure, age of smoking initiation, and their interaction with genetic risk levels with cancer incidence in 393,081 participants from the UK Biobank. Information on tobacco exposure was obtained by self-reported questionnaires. A cancer polygenic risk score was constructed by weighting and integrating 702 genome-wide association studies-identified risk variants. Cox proportional hazards regression models were used to calculate hazard ratios (HRs) for overall cancer and organ-specific cancer incidence. RESULTS During 11.8 years of follow-up, 23,450 (5.97%) and 23,413 (6.03%) incident cancers were included in the analyses of in utero exposure and age of smoking initiation, respectively. The HR (95% CI) for incident cancer in participants with in utero exposure to tobacco smoke was 1.04 (1.01-1.07) for overall cancer, 1.59 (1.44-1.75) for respiratory cancer, and 1.09 (1.03-1.17) for gastrointestinal cancer. The relative risk of incident cancer increased with earlier smoking initiation (Ptrend<.001), with the HR (95% CI) of 1.44 (1.36-1.51) for overall cancer, 13.28 (11.39-15.48) for respiratory cancer, and 1.72 (1.54-1.91) for gastrointestinal cancer in smokers with initiation in childhood compared with never smokers. Importantly, a positive additive interaction between age of smoking initiation and genetic risk was observed for overall cancer (Padditive=.04) and respiratory cancer (Padditive=.003) incidence. CONCLUSION In utero exposure and earlier smoking initiation are associated with overall and organ-specific cancer, and age of smoking initiation interaction with genetic risk is associated with respiratory cancer.
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Affiliation(s)
- Heng He
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China
| | - Qian Shen
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ming-Ming He
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Weihong Qiu
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China
| | - Haoxue Wang
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shanshan Zhang
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shifan Qin
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zequn Lu
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ying Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, China
| | - Jianbo Tian
- Department of Epidemiology and Biostatistics, School of Public Health, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, China
| | - Jiang Chang
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kai Wang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Xuehong Zhang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Xiaoping Miao
- Department of Epidemiology and Biostatistics, School of Public Health, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, China
| | - Mingyang Song
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA; Clinical and Translational Epidemiology Unit, Division of Gastroenterology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Rong Zhong
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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50
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Chen K, Zheng T, Chen C, Liu L, Guo Z, Peng Y, Zhang X, Yang Z. Pregnancy Zone Protein Serves as a Prognostic Marker and Favors Immune Infiltration in Lung Adenocarcinoma. Biomedicines 2023; 11:1978. [PMID: 37509617 PMCID: PMC10377424 DOI: 10.3390/biomedicines11071978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 07/06/2023] [Accepted: 07/11/2023] [Indexed: 07/30/2023] Open
Abstract
Lung adenocarcinoma (LUAD) is a public enemy with a very high incidence and mortality rate, for which there is no specific detectable biomarker. Pregnancy zone protein (PZP) is an immune-related protein; however, the functions of PZP in LUAD are unclear. In this study, a series of bioinformatics methods, combined with immunohistochemistry (IHC), four-color multiplex fluorescence immunohistochemistry (mIHC), quantitative real-time PCR (qRT-PCR) and enzyme-linked immunosorbent assay (ELISA), were utilized to explore the prognostic value and potential role of PZP in LUAD. Our data revealed that PZP expression was markedly reduced in LUAD tissues, tightly correlated with clinical stage and could be an independent unfavorable prognostic factor. In addition, pathway analysis revealed that high expression of PZP in LUAD was mainly involved in immune-related molecules. Tumor immune infiltration analysis by CIBERSORT showed a significant correlation between PZP expression and several immune cell infiltrations, and IHC further confirmed a positive correlation with CD4+ T-cell infiltration and a negative correlation with CD68+ M0 macrophage infiltration. Furthermore, mIHC demonstrated that PZP expression gave rise to an increase in CD86+ M1 macrophages and a decrease in CD206+ M2 macrophages. Therefore, PZP can be used as a new biomarker for the prediction of prognosis and may be a promising immune-related molecular target for LUAD.
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Affiliation(s)
- Kehong Chen
- Department of Cancer Center, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
| | - Taihao Zheng
- Department of Cancer Center, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
| | - Cai Chen
- Department of Cancer Center, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
| | - Liangzhong Liu
- Department of Cancer Center, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
| | - Zhengjun Guo
- Department of Cancer Center, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
| | - Yuan Peng
- Department of Cancer Center, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
| | - Xiaoyue Zhang
- Department of Cancer Center, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
| | - Zhenzhou Yang
- Department of Cancer Center, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
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