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Hsieh AR, Luo YL, Bao BY, Chou TC. Comparative analysis of genetic risk scores for predicting biochemical recurrence in prostate cancer patients after radical prostatectomy. BMC Urol 2024; 24:136. [PMID: 38956663 DOI: 10.1186/s12894-024-01524-6] [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: 12/06/2023] [Accepted: 06/25/2024] [Indexed: 07/04/2024] Open
Abstract
BACKGROUND In recent years, Genome-Wide Association Studies (GWAS) has identified risk variants related to complex diseases, but most genetic variants have less impact on phenotypes. To solve the above problems, methods that can use variants with low genetic effects, such as genetic risk score (GRS), have been developed to predict disease risk. METHODS As the GRS model with the most incredible prediction power for complex diseases has not been determined, our study used simulation data and prostate cancer data to explore the disease prediction power of three GRS models, including the simple count genetic risk score (SC-GRS), the direct logistic regression genetic risk score (DL-GRS), and the explained variance weighted GRS based on directed logistic regression (EVDL-GRS). RESULTS AND CONCLUSIONS We used 26 SNPs to establish GRS models to predict the risk of biochemical recurrence (BCR) after radical prostatectomy. Combining clinical variables such as age at diagnosis, body mass index, prostate-specific antigen, Gleason score, pathologic T stage, and surgical margin and GRS models has better predictive power for BCR. The results of simulation data (statistical power = 0.707) and prostate cancer data (area under curve = 0.8462) show that DL-GRS has the best prediction performance. The rs455192 was the most relevant locus for BCR (p = 2.496 × 10-6) in our study.
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Affiliation(s)
- Ai-Ru Hsieh
- Department of Statistics, Tamkang University, New Taipei City, 251301, Taiwan.
| | - Yi-Ling Luo
- Department of Public Health, College of Public Health, China Medical University, Taichung, 40402, Taiwan
| | - Bo-Ying Bao
- School of Pharmacy, China Medical University, Taichung, 406040, Taiwan
- Department of Nursing, Asia University, Taichung, 41354, Taiwan
| | - Tzu-Chieh Chou
- Department of Public Health, College of Public Health, China Medical University, Taichung, 40402, Taiwan
- Department of Health Risk Management, College of Public Health, China Medical University, Taichung, 40402, Taiwan
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Liu H, Li K, Xia J, Zhu J, Cheng Y, Zhang X, Ye H, Wang P. Prediction of esophageal cancer risk based on genetic variants and environmental risk factors in Chinese population. BMC Cancer 2024; 24:598. [PMID: 38755535 PMCID: PMC11100074 DOI: 10.1186/s12885-024-12370-y] [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: 10/14/2023] [Accepted: 05/10/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Results regarding whether it is essential to incorporate genetic variants into risk prediction models for esophageal cancer (EC) are inconsistent due to the different genetic backgrounds of the populations studied. We aimed to identify single-nucleotide polymorphisms (SNPs) associated with EC among the Chinese population and to evaluate the performance of genetic and non-genetic factors in a risk model for developing EC. METHODS A meta-analysis was performed to systematically identify potential SNPs, which were further verified by a case-control study. Three risk models were developed: a genetic model with weighted genetic risk score (wGRS) based on promising SNPs, a non-genetic model with environmental risk factors, and a combined model including both genetic and non-genetic factors. The discrimination ability of the models was compared using the area under the receiver operating characteristic curve (AUC) and the net reclassification index (NRI). The Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to assess the goodness-of-fit of the models. RESULTS Five promising SNPs were ultimately utilized to calculate the wGRS. Individuals in the highest quartile of the wGRS had a 4.93-fold (95% confidence interval [CI]: 2.59 to 9.38) increased risk of EC compared with those in the lowest quartile. The genetic or non-genetic model identified EC patients with AUCs ranging from 0.618 to 0.650. The combined model had an AUC of 0.707 (95% CI: 0.669 to 0.743) and was the best-fitting model (AIC = 750.55, BIC = 759.34). The NRI improved when the wGRS was added to the risk model with non-genetic factors only (NRI = 0.082, P = 0.037). CONCLUSIONS Among the three risk models for EC, the combined model showed optimal predictive performance and can help to identify individuals at risk of EC for tailored preventive measures.
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Affiliation(s)
- Haiyan Liu
- Department of Epidemiology and Statistics, College of Public Health, Zhengzhou University, Zhengzhou City, 450001, Henan Province, China
- Henan Key Laboratory of Tumor Epidemiology and State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, Zhengzhou City, 450052, Henan Province, China
| | - Keming Li
- Zhengzhou Center for Disease Control and Prevention, Zhengzhou City, 450042, Henan Province, China
| | - Junfen Xia
- Office of Health Care, the Third Affiliated Hospital of Zhengzhou University, Zhengzhou City, 450052, Henan Province, China
| | - Jicun Zhu
- Department of Pharmacy, the First Affiliated Hospital of Zhengzhou University, Zhengzhou City, 450052, Henan Province, China
| | - Yifan Cheng
- Department of Epidemiology and Statistics, College of Public Health, Zhengzhou University, Zhengzhou City, 450001, Henan Province, China
- Henan Key Laboratory of Tumor Epidemiology and State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, Zhengzhou City, 450052, Henan Province, China
| | - Xiaoyue Zhang
- Department of Epidemiology and Statistics, College of Public Health, Zhengzhou University, Zhengzhou City, 450001, Henan Province, China
- Henan Key Laboratory of Tumor Epidemiology and State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, Zhengzhou City, 450052, Henan Province, China
| | - Hua Ye
- Department of Epidemiology and Statistics, College of Public Health, Zhengzhou University, Zhengzhou City, 450001, Henan Province, China
- Henan Key Laboratory of Tumor Epidemiology and State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, Zhengzhou City, 450052, Henan Province, China
| | - Peng Wang
- Department of Epidemiology and Statistics, College of Public Health, Zhengzhou University, Zhengzhou City, 450001, Henan Province, China.
- Henan Key Laboratory of Tumor Epidemiology and State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou University, Zhengzhou City, 450052, Henan Province, China.
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陈 睿, 王 静, 王 硕, 唐 思, 索 晨. [Construction of a Risk Prediction Model for Lung Cancer Based on Lifestyle Behaviors in the UK Biobank Large-Scale Population Cohort]. SICHUAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF SICHUAN UNIVERSITY. MEDICAL SCIENCE EDITION 2023; 54:892-898. [PMID: 37866943 PMCID: PMC10579072 DOI: 10.12182/20230960209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Indexed: 10/24/2023]
Abstract
Objective To identify the risk factors related to lifestyle behaviors that affect the incidence of lung cancer, to build a lung cancer risk prediction model to identify, in the population, individuals who are at high risk, and to facilitate the early detection of lung cancer. Methods The data used in the study were obtained from the UK Biobank, a database that contains information collected from 502 389 participants between March 2006 and October 2010. Based on domestic and international guidelines for lung cancer screening and high-quality research literature on lung cancer risk factors, high-risk population identification criteria were determined. Univariate Cox regression was performed to screen for risk factors of lung cancer and a multifactor lung cancer risk prediction model was constructed using Cox proportional hazards regression. Based on the comparison of Akaike information criterion and Schoenfeld residual test results, the optimal fitted model assuming proportional hazards was selected. The multiple factor Cox proportional hazards regression was performed to consider the survival time and the population was randomly divided into a training set and a validation set by a ratio of 7:3. The model was built using the training set and the performance of the model was internally validated using the validation set. The area under the receiver operating characteristic (ROC) curve ( AUC) was used to evaluate the efficacy of the model. The population was categorized into low-risk, moderate-risk, and high-risk groups based on the probability of occurrence of 0% to <25%, 25% to <75%, and 75% to 100%. The respective proportions of affected individuals in each risk group were calculated. Results The study eventually covered 453 558 individuals, and out of the cumulative follow-up of 5 505 402 person-years, a total of 2 330 cases of lung cancer were diagnosed. Cox proportional hazards regression was performed to identify 10 independent variables as predictors of lung cancer, including age, body mass index (BMI), education, income, physical activity, smoking status, alcohol consumption frequency, fresh fruit intake, family history of cancer, and tobacco exposure, and a model was established accordingly. Internal validation results showed that 8 independent variables (all the 10 independent variables screened out except for BMI and fresh fruit intake) were significant influencing factors of lung cancer ( P<0.05). The AUC of the training set for predicting lung cancer occurrence at one year, five years, and ten years were 0.825, 0.785, and 0.777, respectively. The AUC of the validation set for predicting lung cancer occurrence at one year, five years, and ten years were 0.857, 0.782, and 0.765, respectively. 68.38% of the individuals who might develop lung cancer in the future could be identified by screening the high-risk population. Conclusion We established, in this study, a model for predicting lung cancer risks associated with lifestyle behaviors of a large population. Showing good performance in discriminatory ability, the model can be used as a tool for developing standardized screening strategies for lung cancer.
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Affiliation(s)
- 睿琳 陈
- 复旦大学公共卫生学院 流行病学教研室 (上海 200032)Department of Epidemiology, School of Public Health and Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai 200032, China
| | - 静茹 王
- 复旦大学公共卫生学院 流行病学教研室 (上海 200032)Department of Epidemiology, School of Public Health and Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai 200032, China
| | - 硕 王
- 复旦大学公共卫生学院 流行病学教研室 (上海 200032)Department of Epidemiology, School of Public Health and Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai 200032, China
| | - 思琦 唐
- 复旦大学公共卫生学院 流行病学教研室 (上海 200032)Department of Epidemiology, School of Public Health and Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai 200032, China
| | - 晨 索
- 复旦大学公共卫生学院 流行病学教研室 (上海 200032)Department of Epidemiology, School of Public Health and Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai 200032, China
- 上海市重大传染病和生物安全研究院 (上海 200032)Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai 200032, China
- 复旦大学泰州健康科学研究院 (泰州 225316)Fudan University Taizhou Institute of Health Sciences, Taizhou 225316, China
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Wu X, Huang G, Li W, Chen Y. Ethnicity-specific association between TERT rs2736100 (A > C) polymorphism and lung cancer risk: a comprehensive meta-analysis. Sci Rep 2023; 13:13271. [PMID: 37582820 PMCID: PMC10427644 DOI: 10.1038/s41598-023-40504-y] [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: 01/11/2023] [Accepted: 08/11/2023] [Indexed: 08/17/2023] Open
Abstract
The rs2736100 (A > C) polymorphism of the second intron of Telomerase reverse transcriptase (TERT) has been confirmed to be closely associated with the risk of Lung cancer (LC), but there is still no unified conclusion on the results of its association with LC. This study included Genome-wide association studies (GWAS) and case-control studies reported so far on this association between TERT rs2736100 polymorphism and LC to clarify such a correlation with LC and the differences in it between different ethnicities and different types of LC. Relevant literatures published before May 7, 2022 on 'TERT rs2736100 polymorphism and LC susceptibility' in PubMed, EMbase, CENTRAL, MEDLINE databases were searched through the Internet, and data were extracted. Statistical analysis of data was performed in Revman5.3 software, including drawing forest diagrams, drawing funnel diagrams and so on. Sensitivity and publication bias analysis were performed in Stata 12.0 software. The C allele of TERT rs2736100 was associated with the risk of LC (Overall population: [OR] = 1.21, 95%CI [1.17, 1.25]; Caucasians: [OR] = 1.11, 95%CI [1.06, 1.17]; Asians: [OR] = 1.26, 95%CI [1.21, 1.30]), and Asians had a higher risk of LC than Caucasians (C vs. A: Caucasians: [OR] = 1.11 /Asians: [OR]) = 1.26). The other gene models also showed similar results. The results of stratified analysis of LC patients showed that the C allele was associated with the risk of Non-small-cell lung carcinoma (NSCLC) and Lung adenocarcinoma (LUAD), and the risk of NSCLC and LUAD in Asians was higher than that in Caucasians. The C allele was associated with the risk of Lung squamous cell carcinoma (LUSC) and Small cell lung carcinoma(SCLC) in Asians but not in Caucasians. NSCLC patients ([OR] = 1.27) had a stronger correlation than SCLC patients ([OR] = 1.03), and LUAD patients ([OR] = 1.32) had a stronger correlation than LUSC patients ([OR] = 1.09).In addition, the C allele of TERT rs2736100 was associated with the risk of LC, NSCLC and LUAD in both smoking groups and non-smoking groups, and the risk of LC in non-smokers of different ethnic groups was higher than that in smokers. In the Asians, non-smoking women were more at risk of developing LUAD. The C allele of TERT rs2736100 is a risk factor for LC, NSCLC, and LUAD in different ethnic groups, and the Asian population is at a more common risk. The C allele is a risk factor for LUSC and SCLC in Asians but not in Caucasians. And smoking is not the most critical factor that causes variation in TERT rs2736100 to increase the risk of most LC (NSCLC, LUAD). Therefore, LC is a multi-etiological disease caused by a combination of genetic, environmental and lifestyle factors.
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Affiliation(s)
- Xiaozheng Wu
- Department of Preclinical Medicine, Guizhou University of Traditional Chinese Medicine, Guiyang, 510025, China
| | - Gao Huang
- Department of Preclinical Medicine, Guizhou University of Traditional Chinese Medicine, Guiyang, 510025, China
| | - Wen Li
- Department of Preclinical Medicine, Guizhou University of Traditional Chinese Medicine, Guiyang, 510025, China
| | - Yunzhi Chen
- Department of Preclinical Medicine, Guizhou University of Traditional Chinese Medicine, Guiyang, 510025, China.
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5
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Guo L, Meng Q, Zheng L, Chen Q, Liu Y, Xu H, Kang R, Zhang L, Liu S, Sun X, Zhang S. Lung Cancer Risk Prediction Nomogram in Nonsmoking Chinese Women: Retrospective Cross-sectional Cohort Study. JMIR Public Health Surveill 2023; 9:e41640. [PMID: 36607729 PMCID: PMC9862335 DOI: 10.2196/41640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 11/04/2022] [Accepted: 11/25/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND It is believed that smoking is not the cause of approximately 53% of lung cancers diagnosed in women globally. OBJECTIVE The study aimed to develop and validate a simple and noninvasive model that could assess and stratify lung cancer risk in nonsmoking Chinese women. METHODS Based on the population-based Cancer Screening Program in Urban China, this retrospective, cross-sectional cohort study was carried out with a vast population base and an immense number of participants. The training set and the validation set were both constructed using a random distribution of the data. Following the identification of associated risk factors by multivariable Cox regression analysis, a predictive nomogram was developed. Discrimination (area under the curve) and calibration were further performed to assess the validation of risk prediction nomogram in the training set, which was then validated in the validation set. RESULTS In sum, 151,834 individuals signed up to take part in the survey. Both the training set (n=75,917) and the validation set (n=75,917) were comprised of randomly selected participants. Potential predictors for lung cancer included age, history of chronic respiratory disease, first-degree family history of lung cancer, menopause, and history of benign breast disease. We displayed 1-year, 3-year, and 5-year lung cancer risk-predicting nomograms using these 5 factors. In the training set, the 1-year, 3-year, and 5-year lung cancer risk areas under the curve were 0.762, 0.718, and 0.703, respectively. In the validation set, the model showed a moderate predictive discrimination. CONCLUSIONS We designed and validated a simple and noninvasive lung cancer risk model for nonsmoking women. This model can be applied to identify and triage people at high risk for developing lung cancers among nonsmoking women.
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Affiliation(s)
- Lanwei Guo
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Qingcheng Meng
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Liyang Zheng
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Qiong Chen
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Yin Liu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Huifang Xu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Ruihua Kang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Luyao Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Shuzheng Liu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Xibin Sun
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
| | - Shaokai Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China
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6
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Wang F, Tan F, Shen S, Wu Z, Cao W, Yu Y, Dong X, Xia C, Tang W, Xu Y, Qin C, Zhu M, Li J, Yang Z, Zheng Y, Luo Z, Zhao L, Li J, Ren J, Shi J, Huang Y, Wu N, Shen H, Chen W, Li N, He J. Risk-stratified Approach for Never- and Ever-Smokers in Lung Cancer Screening: A Prospective Cohort Study in China. Am J Respir Crit Care Med 2023; 207:77-88. [PMID: 35900139 DOI: 10.1164/rccm.202204-0727oc] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
Rationale: Over 40% of lung cancer cases occurred in never-smokers in China. However, high-risk never-smokers were precluded from benefiting from lung cancer screening as most screening guidelines did not consider them. Objectives: We sought to develop and validate prediction models for 3-year lung cancer risks for never- and ever-smokers, named the China National Cancer Center Lung Cancer models (China NCC-LCm2021 models). Methods: 425,626 never-smokers and 128,952 ever-smokers from the National Lung Cancer Screening program were used as the training cohort and analyzed using multivariable Cox models. Models were validated in two independent prospective cohorts: one included 369,650 never-smokers and 107,678 ever-smokers (841 and 421 lung cancers), and the other included 286,327 never-smokers and 78,469 ever-smokers (503 and 127 lung cancers). Measurements and Main Results: The areas under the receiver operating characteristic curves in the two validation cohorts were 0.698 and 0.673 for never-smokers and 0.728 and 0.752 for ever-smokers. Our models had higher areas under the receiver operating characteristic curves than other existing models and were well calibrated in the validation cohort. The China NCC-LCm2021 ⩾0.47% threshold was suggested for never-smokers and ⩾0.51% for ever-smokers. Moreover, we provided a range of threshold options with corresponding expected screening outcomes, screening targets, and screening efficiency. Conclusion: The construction of the China NCC-LCm2021 models can accurately reflect individual risk of lung cancer, regardless of smoking status. Our models can significantly increase the feasibility of conducting centralized lung cancer screening programs because we provide justified thresholds to define the high-risk population of lung cancer and threshold options to adapt different configurations of medical resources.
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Affiliation(s)
| | | | - Sipeng Shen
- School of Public Health, and.,Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | | | | | | | | | | | - Wei Tang
- Department of Diagnostic Radiology
| | | | | | - Meng Zhu
- School of Public Health, and.,Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | | | | | | | | | | | | | | | | | | | - Ning Wu
- Department of Diagnostic Radiology.,PET-CT center
| | - Hongbing Shen
- School of Public Health, and.,Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | | | - Ni Li
- Office of Cancer Screening.,Key Laboratory of Cancer Data Science, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; and
| | - Jie He
- Department of Thoracic Surgery
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7
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Li Y, Zou Z, Gao Z, Wang Y, Xiao M, Xu C, Jiang G, Wang H, Jin L, Wang J, Wang HZ, Guo S, Wu J. Prediction of lung cancer risk in Chinese population with genetic-environment factor using extreme gradient boosting. Cancer Med 2022; 11:4469-4478. [PMID: 35499292 PMCID: PMC9741969 DOI: 10.1002/cam4.4800] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 04/22/2022] [Accepted: 04/24/2022] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Detecting early-stage lung cancer is critical to reduce the lung cancer mortality rate; however, existing models based on germline variants perform poorly, and new models are needed. This study aimed to use extreme gradient boosting to develop a predictive model for the early diagnosis of lung cancer in a multicenter case-control study. MATERIALS AND METHODS A total of 974 cases and 1005 controls in Shanghai and Taizhou were recruited, and 61 single nucleotide polymorphisms (SNPs) were genotyped. Multivariate logistic regression was used to calculate the association between signal SNPs and lung cancer risk. Logistic regression (LR) and extreme gradient boosting (XGBoost) algorithms, a large-scale machine learning algorithm, were adopted to build the lung cancer risk model. In both models, 10-fold cross-validation was performed, and model predictive performance was evaluated by the area under the curve (AUC). RESULTS After FDR adjustment, TYMS rs3819102 and BAG6 rs1077393 were significantly associated with lung cancer risk (p < 0.05). For lung cancer risk prediction, the model predicted only with epidemiology attained an AUC of 0.703 for LR and 0.744 for XGBoost. Compared with the LR model predicted only with epidemiology, further adding SNPs and applying XGBoost increased the AUC to 0.759 (p < 0.001) in the XGBoost model. BAG6 rs1077393 was the most important predictor among all SNPs in the lung cancer prediction XGBoost model, followed by TERT rs2735845 and CAMKK1 rs7214723. Further stratification in lung adenocarcinoma (ADC) showed a significantly elevated performance from 0.639 to 0.699 (p = 0.009) when applying XGBoost and adding SNPs to the model, while the best model for lung squamous cell carcinoma (SCC) prediction was the LR model predicted with epidemiology and SNPs (AUC = 0.833), compared with the XGBoost model (AUC = 0.816). CONCLUSION Our lung cancer risk prediction models in the Chinese population have a strong predictive ability, especially for SCC. Adding SNPs and applying the XGBoost algorithm to the epidemiologic-based logistic regression risk prediction model significantly improves model performance.
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Affiliation(s)
- Yutao Li
- School of Life SciencesFudan UniversityShanghaiChina
| | - Zixiu Zou
- School of Life SciencesFudan UniversityShanghaiChina
| | - Zhunyi Gao
- Company 6 of Basic Medical SchoolNavy Military Medical UniversityShanghaiChina
| | - Yi Wang
- School of Life SciencesFudan UniversityShanghaiChina
| | - Man Xiao
- Department of Biochemistry and Molecular BiologyHainan Medical UniversityHaikouChina
| | - Chang Xu
- Clinical College of Xiangnan UniversityChenzhouChina
| | - Gengxi Jiang
- Department of Thoracic Surgerythe First Affiliated Hospital of Naval Medical University (Second Military Medical University)ShanghaiChina
| | - Haijian Wang
- School of Life SciencesFudan UniversityShanghaiChina
| | - Li Jin
- School of Life SciencesFudan UniversityShanghaiChina
| | - Jiucun Wang
- School of Life SciencesFudan UniversityShanghaiChina
| | - Huai Zhou Wang
- Department of Laboratory Diagnosisthe First Affiliated Hospital of Naval Medical University (Second Military Medical University)ShanghaiChina
| | - Shicheng Guo
- School of Life SciencesFudan UniversityShanghaiChina
| | - Junjie Wu
- School of Life SciencesFudan UniversityShanghaiChina,Department of Pulmonary and Critical Care Medicine, Zhongshan HospitalFudan UniversityShanghaiChina,Department of Pulmonary and Critical Care MedicineShanghai Geriatric Medical CenterShanghaiChina
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8
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He YQ, Wang TM, Ji M, Mai ZM, Tang M, Wang R, Zhou Y, Zheng Y, Xiao R, Yang D, Wu Z, Deng C, Zhang J, Xue W, Dong S, Zhan J, Cai Y, Li F, Wu B, Liao Y, Zhou T, Zheng M, Jia Y, Li D, Cao L, Yuan L, Zhang W, Luo L, Tong X, Wu Y, Li X, Zhang P, Zheng X, Zhang S, Hu Y, Qin W, Deng B, Liang X, Fan P, Feng Y, Song J, Xie SH, Chang ET, Zhang Z, Huang G, Xu M, Feng L, Jin G, Bei J, Cao S, Liu Q, Kozlakidis Z, Mai H, Sun Y, Ma J, Hu Z, Liu J, Lung ML, Adami HO, Shen H, Ye W, Lam TH, Zeng YX, Jia WH. A polygenic risk score for nasopharyngeal carcinoma shows potential for risk stratification and personalized screening. Nat Commun 2022; 13:1966. [PMID: 35414057 PMCID: PMC9005522 DOI: 10.1038/s41467-022-29570-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 03/23/2022] [Indexed: 12/29/2022] Open
Abstract
Polygenic risk scores (PRS) have the potential to identify individuals at risk of diseases, optimizing treatment, and predicting survival outcomes. Here, we construct and validate a genome-wide association study (GWAS) derived PRS for nasopharyngeal carcinoma (NPC), using a multi-center study of six populations (6 059 NPC cases and 7 582 controls), and evaluate its utility in a nested case-control study. We show that the PRS enables effective identification of NPC high-risk individuals (AUC = 0.65) and improves the risk prediction with the PRS incremental deciles in each population (Ptrend ranging from 2.79 × 10-7 to 4.79 × 10-44). By incorporating the PRS into EBV-serology-based NPC screening, the test's positive predictive value (PPV) is increased from an average of 4.84% to 8.38% and 11.91% in the top 10% and 5% PRS, respectively. In summary, the GWAS-derived PRS, together with the EBV test, significantly improves NPC risk stratification and informs personalized screening.
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Affiliation(s)
- Yong-Qiao He
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Tong-Min Wang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Mingfang Ji
- Cancer Research Institute of Zhongshan City, Zhongshan Hospital of Sun Yat-sen University, Zhongshan, China
| | - Zhi-Ming Mai
- School of Public Health, The University of Hong Kong, Hong Kong S.A.R., China
- Center for Nasopharyngeal Carcinoma Research (CNPCR), The University of Hong Kong, Hong Kong S.A.R., China
- Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Minzhong Tang
- Wuzhou Red Cross Hospital, Wuzhou, Guangxi, P.R. China
- Wuzhou Cancer Center, Wuzhou, Guangxi, P.R. China
| | - Ruozheng Wang
- Key Laboratory of Cancer Immunotherapy and Radiotherapy, Chinese Academy of Medical Sciences, Ürümqi, Xinjiang Uygur Autonomous Region, 830011, P.R. China
| | - Yifeng Zhou
- Department of Genetics, Medical College of Soochow University, Suzhou, China
| | - Yuming Zheng
- Wuzhou Red Cross Hospital, Wuzhou, Guangxi, P.R. China
- Wuzhou Cancer Center, Wuzhou, Guangxi, P.R. China
| | - Ruowen Xiao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Dawei Yang
- School of Public Health, Sun Yat-sen University, Guangzhou, P.R. China
| | - Ziyi Wu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Changmi Deng
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Jiangbo Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Wenqiong Xue
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Siqi Dong
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Jiyun Zhan
- Public Health Service Center of Xiaolan Town, Zhongshan City, Guangdong, China
| | - Yonglin Cai
- Wuzhou Red Cross Hospital, Wuzhou, Guangxi, P.R. China
| | - Fugui Li
- Cancer Research Institute of Zhongshan City, Zhongshan Hospital of Sun Yat-sen University, Zhongshan, China
| | - Biaohua Wu
- Cancer Research Institute of Zhongshan City, Zhongshan Hospital of Sun Yat-sen University, Zhongshan, China
| | - Ying Liao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Ting Zhou
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Meiqi Zheng
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Yijing Jia
- School of Public Health, Sun Yat-sen University, Guangzhou, P.R. China
| | - Danhua Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Lianjing Cao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Leilei Yuan
- School of Public Health, Sun Yat-sen University, Guangzhou, P.R. China
| | - Wenli Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Luting Luo
- School of Public Health, Sun Yat-sen University, Guangzhou, P.R. China
| | - Xiating Tong
- School of Public Health, Sun Yat-sen University, Guangzhou, P.R. China
| | - Yanxia Wu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Xizhao Li
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Peifen Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Xiaohui Zheng
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Shaodan Zhang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Yezhu Hu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Weiling Qin
- Wuzhou Red Cross Hospital, Wuzhou, Guangxi, P.R. China
| | - Bisen Deng
- Public Health Service Center of Xiaolan Town, Zhongshan City, Guangdong, China
| | - Xuejun Liang
- Public Health Service Center of Xiaolan Town, Zhongshan City, Guangdong, China
| | - Peiwen Fan
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Departments of Institute for Cancer Research, The Third Affiliated Hospital of Xinjiang Medical University, Ürümqi, 830011, P.R. China
| | - Yaning Feng
- Key Laboratory of Oncology of Xinjiang Uyghur Autonomous Region, Ürümqi, 830011, China
| | - Jia Song
- Departments of Institute for Cancer Research, The Third Affiliated Teaching Hospital of Xinjiang Medical University, Affiliated Cancer Hospital, Ürümqi, Xinjiang Uyghur Autonomous Region, 830010, P.R. China
| | - Shang-Hang Xie
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Ellen T Chang
- Center for Health Sciences, Exponent, Inc., Menlo Park, CA, USA
- Stanford Cancer Institute, Stanford, CA, USA
| | - Zhe Zhang
- Department of Otolaryngology-Head and Neck Surgery, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Guangwu Huang
- Department of Otolaryngology-Head and Neck Surgery, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
| | - Miao Xu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Lin Feng
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Guangfu Jin
- Department of Epidemiology, International Joint Research Center on Environment and Human Health, Center for Global Health, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Jinxin Bei
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Sumei Cao
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Qing Liu
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Zisis Kozlakidis
- Division of Infection and Immunity, Faculty of Medical Sciences - University College London, London, UK
- International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Haiqiang Mai
- Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Ying Sun
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Jun Ma
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, China
| | - Zhibin Hu
- Department of Epidemiology, International Joint Research Center on Environment and Human Health, Center for Global Health, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Jianjun Liu
- Human Genetics, Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Maria Li Lung
- Center for Nasopharyngeal Carcinoma Research (CNPCR), The University of Hong Kong, Hong Kong S.A.R., China
- Department of Clinical Oncology, The University of Hong Kong, Hong Kong S.A.R., China
| | - Hans-Olov Adami
- Clinical Effectiveness Group, Institute of Health and Society, University of Oslo, Oslo, Norway
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Hongbing Shen
- Department of Epidemiology, International Joint Research Center on Environment and Human Health, Center for Global Health, Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China.
| | - Weimin Ye
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
- Department of Epidemiology and Health Statistics & Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, China.
| | - Tai-Hing Lam
- School of Public Health, The University of Hong Kong, Hong Kong S.A.R., China.
- Center for Nasopharyngeal Carcinoma Research (CNPCR), The University of Hong Kong, Hong Kong S.A.R., China.
| | - Yi-Xin Zeng
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Wei-Hua Jia
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China.
- School of Public Health, Sun Yat-sen University, Guangzhou, P.R. China.
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9
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Guo LW, Lyu ZY, Meng QC, Zheng LY, Chen Q, Liu Y, Xu HF, Kang RH, Zhang LY, Cao XQ, Liu SZ, Sun XB, Zhang JG, Zhang SK. Construction and Validation of a Lung Cancer Risk Prediction Model for Non-Smokers in China. Front Oncol 2022; 11:766939. [PMID: 35059311 PMCID: PMC8764453 DOI: 10.3389/fonc.2021.766939] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 12/13/2021] [Indexed: 11/13/2022] Open
Abstract
Background About 15% of lung cancers in men and 53% in women are not attributable to smoking worldwide. The aim was to develop and validate a simple and non-invasive model which could assess and stratify lung cancer risk in non-smokers in China. Methods A large-sample size, population-based study was conducted under the framework of the Cancer Screening Program in Urban China (CanSPUC). Data on the lung cancer screening in Henan province, China, from October 2013 to October 2019 were used and randomly divided into the training and validation sets. Related risk factors were identified through multivariable Cox regression analysis, followed by establishment of risk prediction nomogram. Discrimination [area under the curve (AUC)] and calibration were further performed to assess the validation of risk prediction nomogram in the training set, and then validated by the validation set. Results A total of 214,764 eligible subjects were included, with a mean age of 55.19 years. Subjects were randomly divided into the training (107,382) and validation (107,382) sets. Elder age, being male, a low education level, family history of lung cancer, history of tuberculosis, and without a history of hyperlipidemia were the independent risk factors for lung cancer. Using these six variables, we plotted 1-year, 3-year, and 5-year lung cancer risk prediction nomogram. The AUC was 0.753, 0.752, and 0.755 for the 1-, 3- and 5-year lung cancer risk in the training set, respectively. In the validation set, the model showed a moderate predictive discrimination, with the AUC was 0.668, 0.678, and 0.685 for the 1-, 3- and 5-year lung cancer risk. Conclusions We developed and validated a simple and non-invasive lung cancer risk model in non-smokers. This model can be applied to identify and triage patients at high risk for developing lung cancers in non-smokers.
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Affiliation(s)
- Lan-Wei Guo
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhang-Yan Lyu
- Department of Cancer Epidemiology and Biostatistics, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy of the Ministry of Education, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Qing-Cheng Meng
- Department of Radiology, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Li-Yang Zheng
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Qiong Chen
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Yin Liu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Hui-Fang Xu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Rui-Hua Kang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Lu-Yao Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiao-Qin Cao
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Shu-Zheng Liu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Xi-Bin Sun
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Jian-Gong Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
| | - Shao-Kai Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou, China
- *Correspondence: Shao-Kai Zhang,
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10
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Guo LW, Lyu ZY, Meng QC, Zheng LY, Chen Q, Liu Y, Xu HF, Kang RH, Zhang LY, Cao XQ, Liu SZ, Sun XB, Zhang JG, Zhang SK. A risk prediction model for selecting high-risk population for computed tomography lung cancer screening in China. Lung Cancer 2021; 163:27-34. [PMID: 34894456 DOI: 10.1016/j.lungcan.2021.11.015] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 11/18/2021] [Accepted: 11/22/2021] [Indexed: 01/22/2023]
Abstract
OBJECTIVE Two large randomized controlled trials (RCTs) have demonstrated that low dose computed tomography (LDCT) screening reduces lung cancer mortality. Risk-prediction models have been proved to select individuals for lung cancer screening effectively. With the focus on established risk factors for lung cancer routinely available in general cancer screening settings, we aimed to develop and internally validated a risk prediction model for lung cancer. MATERIALS AND METHODS Using data from the Cancer Screening Program in Urban China (CanSPUC) in Henan province, China between 2013 and 2019, we conducted a prospective cohort study consisting of 282,254 participants including 126,445 males and 155,809 females. Detailed questionnaire, physical assessment and follow-up were completed for all participants. Using Cox proportional risk regression analysis, we developed the Henan Lung Cancer Risk Models based on simplified questionnaire. Model discrimination was evaluated by concordance statistics (C-statistics), and model calibration was evaluated by the bootstrap sampling, respectively. RESULTS By 2020, a total of 589 lung cancer cases occurred in the follow-up yielding an incident density of 64.91/100,000 person-years (pyrs). Age, gender, smoking, history of tuberculosis and history of emphysema were included into the model. The C-index of the model for 1-year lung cancer risk was 0.766 and 0.741 in the training set and validation set, respectively. In stratified analysis, the model showed better predictive power in males, younger participants, and former or current smoking participants. The model calibrated well across the deciles of predicted risk in both the overall population and all subgroups. CONCLUSIONS We developed and internally validated a simple risk prediction model for lung cancer, which may be useful to identify high-risk individuals for more intensive screening for cancer prevention.
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Affiliation(s)
- Lan-Wei Guo
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Zhang-Yan Lyu
- Department of Cancer Epidemiology and Biostatistics, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy of Tianjin, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Molecular Cancer Epidemiology of Tianjin, Key Laboratory of Breast Cancer Prevention and Therapy of the Ministry of Education, Tianjin, China
| | - Qing-Cheng Meng
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Li-Yang Zheng
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Qiong Chen
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Yin Liu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Hui-Fang Xu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Rui-Hua Kang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Lu-Yao Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Xiao-Qin Cao
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Shu-Zheng Liu
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Xi-Bin Sun
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Jian-Gong Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China
| | - Shao-Kai Zhang
- Department of Cancer Epidemiology and Prevention, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, The Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450008, China.
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11
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Veiskarami P, Houshmand M, Seifi S, Ansarinejad N, Fardad F, Abbasi B. The effect of CHRNA3 rs1051730 C>T and ABCB1 rs3842 A>G polymorphisms on non-small cell lung cancer and nicotine dependence in Iranian population. Heliyon 2021; 7:e07867. [PMID: 34522797 PMCID: PMC8426517 DOI: 10.1016/j.heliyon.2021.e07867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 12/14/2020] [Accepted: 08/20/2021] [Indexed: 12/24/2022] Open
Abstract
Aims Lung cancer is still the leading cause of cancer mortality in all over the world. Nicotine and its derivatives are the most well-known carcinogens that participate in both etiology and progression of lung cancer. The objective of the current study was to investigate whether single nucleotide polymorphisms (SNPs) rs1051730C > T in CHRNA3 and rs3842A > G in ABCB1, two genes contributing in the mechanism of disposition and metabolism of nicotine and its derivatives, could modify the risk of developing lung cancer, as well as nicotine dependence in Iranian. Main methods The genotyping analysis for these two SNPs was conducted in a case-control study of 108 lung cancer cases and 120 healthy controls using ARMS-PCR and Tetra-primer ARMS-PCR techniques. The correlation between studied SNPs and lung cancer was assessed by the regression analysis. Key findings We observed a significant association between lung cancer and rs1051730C > T by using four genetic models: allele (OR:1.83; 95% CI:1.24-2.6; p = 0.002), dominant (OR: 2.19; 95% CI:1.27-3.78; p = 0.005), recessive (OR: 2.25; 95% CI: 1.02-4.95; p = 0.043) and additive (TT vs CC: OR:3.25; 95% CI:1.38-7.60; p = 0.007, CT vs CC: OR:1.96; 95% CI:1.10-3.48; p = 0.021). Furthermore, a significant association between this variant and nicotine dependence (OR: 2.27; 95% CI: 1.52-3.39; p = 0.00005) was reported. However, no association was found for rs3842A > G. Significance The results suggested that the CHRNA3 rs1051730C > T via a smoking-dependent manner could modify susceptibility to lung cancer among Iranian population.
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Affiliation(s)
- Parisa Veiskarami
- Department of Medical Genetics, National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran
| | - Massoud Houshmand
- Department of Medical Genetics, National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran.,Research Center, Knowledge University, Erbil, Kurdistan Region, Iraq
| | - Sharareh Seifi
- Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Nafiseh Ansarinejad
- Department of Hematology and Medical Oncology, Rasool Akram Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Farshid Fardad
- Department of Hematology and Medical Oncology, Rasool Akram Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Bahareh Abbasi
- Department of Medical Genetics, National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran
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12
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Sun P, Lu Q, Li Z, Qin N, Jiang Y, Ma H, Jin G, Yu H, Dai J. Assessment of prognostic prediction models for gastric cancer using genomic and transcriptomic profiles. Meta Gene 2021. [DOI: 10.1016/j.mgene.2021.100890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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13
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He CY, Chen LZ, Wang ZX, Sun LP, Peng JJ, Wu MQ, Wang TM, Li YQ, Yang XH, Zhou DL, Ye ZL, Ma JJ, Li XZ, Zhang PF, Ju HQ, Mo HY, Zhang ZC, Zeng ZL, Shao JY, Jia WH, Cai SJ, Yuan Y, Xu RH. Performance of common genetic variants in risk prediction for colorectal cancer in Chinese: A two-stage and multicenter study. Genomics 2021; 113:867-873. [PMID: 33545268 DOI: 10.1016/j.ygeno.2021.01.025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Revised: 01/21/2021] [Accepted: 01/31/2021] [Indexed: 11/25/2022]
Abstract
The efficacy of susceptible variants derived from genome-wide association studies (GWAs) optimizing discriminatory accuracy of colorectal cancer (CRC) in Chinese remains unclear. In the present validation study, we assessed 75 recently identified variants from GWAs. A risk predictive model combining 19 variants using the least absolute shrinkage and selection operator (LASSO) statistics offered certain clinical advantages. This model demonstrated an area under the receiver operating characteristic (AUC) of 0.61 during training analysis and yielded robust AUCs from 0.59 to 0.61 during validation analysis in three independent centers. The individuals carrying the highest quartile of risk score revealed over 2-fold risks of CRC (ranging from 2.12 to 2.90) compared with those who presented the lowest quartile of risk score. This genetic model offered the possibility of partitioning risk within the average risk population, which might serve as a first step toward developing individualized CRC prevention strategies in China.
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Affiliation(s)
- Cai-Yun He
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Le-Zong Chen
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Medical Oncology, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Zi-Xian Wang
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Medical Oncology, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Li-Ping Sun
- Tumor Etiology and Screening Department of Cancer Institute and General Surgery, Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, Key Laboratory of GI Cancer Etiology and Prevention in Liaoning Province, the First Hospital of China Medical University, Shenyang 110001, China
| | - Jun-Jie Peng
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Min-Qing Wu
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Cancer Prevention, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Tong-Min Wang
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Ya-Qi Li
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Xin-Hua Yang
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Da-Lei Zhou
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Zu-Lu Ye
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Jiang-Jun Ma
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Xi-Zhao Li
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Pei-Fen Zhang
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Huai-Qiang Ju
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Hai-Yu Mo
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Zi-Chen Zhang
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Zhao-Lei Zeng
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Jian-Yong Shao
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China
| | - Wei-Hua Jia
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.
| | - San-Jun Cai
- Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China.
| | - Yuan Yuan
- Tumor Etiology and Screening Department of Cancer Institute and General Surgery, Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, Key Laboratory of GI Cancer Etiology and Prevention in Liaoning Province, the First Hospital of China Medical University, Shenyang 110001, China.
| | - Rui-Hua Xu
- Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Medical Oncology, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China.
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14
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Lebrett MB, Crosbie EJ, Smith MJ, Woodward ER, Evans DG, Crosbie PAJ. Targeting lung cancer screening to individuals at greatest risk: the role of genetic factors. J Med Genet 2021; 58:217-226. [PMID: 33514608 PMCID: PMC8005792 DOI: 10.1136/jmedgenet-2020-107399] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 12/06/2020] [Accepted: 12/08/2020] [Indexed: 12/24/2022]
Abstract
Lung cancer (LC) is the most common global cancer. An individual’s risk of developing LC is mediated by an array of factors, including family history of the disease. Considerable research into genetic risk factors for LC has taken place in recent years, with both low-penetrance and high-penetrance variants implicated in increasing or decreasing a person’s risk of the disease. LC is the leading cause of cancer death worldwide; poor survival is driven by late onset of non-specific symptoms, resulting in late-stage diagnoses. Evidence for the efficacy of screening in detecting cancer earlier, thereby reducing lung-cancer specific mortality, is now well established. To ensure the cost-effectiveness of a screening programme and to limit the potential harms to participants, a risk threshold for screening eligibility is required. Risk prediction models (RPMs), which provide an individual’s personal risk of LC over a particular period based on a large number of risk factors, may improve the selection of high-risk individuals for LC screening when compared with generalised eligibility criteria that only consider smoking history and age. No currently used RPM integrates genetic risk factors into its calculation of risk. This review provides an overview of the evidence for LC screening, screening related harms and the use of RPMs in screening cohort selection. It gives a synopsis of the known genetic risk factors for lung cancer and discusses the evidence for including them in RPMs, focusing in particular on the use of polygenic risk scores to increase the accuracy of targeted lung cancer screening.
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Affiliation(s)
- Mikey B Lebrett
- Division of Infection, Immunity and Respiratory Medicine, The University of Manchester Faculty of Biology Medicine and Health, Manchester, UK.,Prevention and Early Detection Theme, NIHR Manchester Biomedical Research Centre, Manchester, UK
| | - Emma J Crosbie
- Prevention and Early Detection Theme, NIHR Manchester Biomedical Research Centre, Manchester, UK.,Division of Cancer Sciences, The University of Manchester Faculty of Biology Medicine and Health, Manchester, UK
| | - Miriam J Smith
- Prevention and Early Detection Theme, NIHR Manchester Biomedical Research Centre, Manchester, UK.,Manchester Centre for Genomic Medicine, St Mary's Hospital, Division of Evolution and Genomic Sciences, School of Biological Sciences, University of Manchester, Manchester, UK
| | - Emma R Woodward
- Prevention and Early Detection Theme, NIHR Manchester Biomedical Research Centre, Manchester, UK.,Manchester Centre for Genomic Medicine, St Mary's Hospital, Division of Evolution and Genomic Sciences, School of Biological Sciences, University of Manchester, Manchester, UK
| | - D Gareth Evans
- Prevention and Early Detection Theme, NIHR Manchester Biomedical Research Centre, Manchester, UK.,Manchester Centre for Genomic Medicine, St Mary's Hospital, Division of Evolution and Genomic Sciences, School of Biological Sciences, University of Manchester, Manchester, UK
| | - Philip A J Crosbie
- Division of Infection, Immunity and Respiratory Medicine, The University of Manchester Faculty of Biology Medicine and Health, Manchester, UK .,Prevention and Early Detection Theme, NIHR Manchester Biomedical Research Centre, Manchester, UK.,Manchester Thoracic Oncology Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, UK
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15
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Toumazis I, Bastani M, Han SS, Plevritis SK. Risk-Based lung cancer screening: A systematic review. Lung Cancer 2020; 147:154-186. [DOI: 10.1016/j.lungcan.2020.07.007] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 07/03/2020] [Accepted: 07/04/2020] [Indexed: 12/17/2022]
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16
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Lyu Z, Li N, Chen S, Wang G, Tan F, Feng X, Li X, Wen Y, Yang Z, Wang Y, Li J, Chen H, Lin C, Ren J, Shi J, Wu S, Dai M, He J. Risk prediction model for lung cancer incorporating metabolic markers: Development and internal validation in a Chinese population. Cancer Med 2020; 9:3983-3994. [PMID: 32253829 PMCID: PMC7286442 DOI: 10.1002/cam4.3025] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 02/20/2020] [Accepted: 03/03/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Low-dose computed tomography screening has been proved to reduce lung cancer mortality, however, the issues of high false-positive rate and overdiagnosis remain unsolved. Risk prediction models for lung cancer that could accurately identify high-risk populations may help to increase efficiency. We thus sought to develop a risk prediction model for lung cancer incorporating epidemiological and metabolic markers in a Chinese population. METHODS During 2006 and 2015, a total of 122 497 people were observed prospectively for lung cancer incidence with the total person-years of 976 663. Stepwise multivariable-adjusted logistic regressions with Pentry = .15 and Pstay = .20 were conducted to select the candidate variables including demographics and metabolic markers such as high-sensitivity C-reactive protein (hsCRP) and low-density lipoprotein cholesterol (LDL-C) into the prediction model. We used the C-statistic to evaluate discrimination, and Hosmer-Lemeshow tests for calibration. Tenfold cross-validation was conducted for internal validation to assess the model's stability. RESULTS A total of 984 lung cancer cases were identified during the follow-up. The epidemiological model including age, gender, smoking status, alcohol intake status, coal dust exposure status, and body mass index generated a C-statistic of 0.731. The full model additionally included hsCRP and LDL-C showed significantly better discrimination (C-statistic = 0.735, P = .033). In stratified analysis, the full model showed better predictive power in terms of C-statistic in younger participants (<50 years, 0.709), females (0.726), and former or current smokers (0.742). The model calibrated well across the deciles of predicted risk in both the overall population (PHL = .689) and all subgroups. CONCLUSIONS We developed and internally validated an easy-to-use risk prediction model for lung cancer among the Chinese population that could provide guidance for screening and surveillance.
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Affiliation(s)
- Zhangyan Lyu
- 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
| | - 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
| | - Shuohua Chen
- Department of Oncology, Kailuan General Hospital, Tangshan, China
| | - Gang Wang
- Health Department of Kailuan (Group), Tangshan, China
| | - Fengwei Tan
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaoshuang Feng
- 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
| | - Xin 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
| | - Yan Wen
- 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
| | - Zhuoyu Yang
- 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
| | - Yalong Wang
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiang 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
| | - Hongda 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
| | - Chunqing Lin
- 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
| | - Jiansong Ren
- 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
| | - Jufang Shi
- 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
| | - Shouling Wu
- Department of Oncology, Kailuan General Hospital, Tangshan, China
| | - Min Dai
- 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
| | - Jie He
- Department of Thoracic Surgery, 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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17
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Patron J, Serra-Cayuela A, Han B, Li C, Wishart DS. Assessing the performance of genome-wide association studies for predicting disease risk. PLoS One 2019; 14:e0220215. [PMID: 31805043 PMCID: PMC6894795 DOI: 10.1371/journal.pone.0220215] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2019] [Accepted: 11/01/2019] [Indexed: 12/24/2022] Open
Abstract
To date more than 3700 genome-wide association studies (GWAS) have been published that look at the genetic contributions of single nucleotide polymorphisms (SNPs) to human conditions or human phenotypes. Through these studies many highly significant SNPs have been identified for hundreds of diseases or medical conditions. However, the extent to which GWAS-identified SNPs or combinations of SNP biomarkers can predict disease risk is not well known. One of the most commonly used approaches to assess the performance of predictive biomarkers is to determine the area under the receiver-operator characteristic curve (AUROC). We have developed an R package called G-WIZ to generate ROC curves and calculate the AUROC using summary-level GWAS data. We first tested the performance of G-WIZ by using AUROC values derived from patient-level SNP data, as well as literature-reported AUROC values. We found that G-WIZ predicts the AUROC with <3% error. Next, we used the summary level GWAS data from GWAS Central to determine the ROC curves and AUROC values for 569 different GWA studies spanning 219 different conditions. Using these data we found a small number of GWA studies with SNP-derived risk predictors that have very high AUROCs (>0.75). On the other hand, the average GWA study produces a multi-SNP risk predictor with an AUROC of 0.55. Detailed AUROC comparisons indicate that most SNP-derived risk predictions are not as good as clinically based disease risk predictors. All our calculations (ROC curves, AUROCs, explained heritability) are in a publicly accessible database called GWAS-ROCS (http://gwasrocs.ca). The G-WIZ code is freely available for download at https://github.com/jonaspatronjp/GWIZ-Rscript/.
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Affiliation(s)
- Jonas Patron
- Department of Biological Sciences, University of Alberta, Edmonton, Canada
| | | | - Beomsoo Han
- Department of Biological Sciences, University of Alberta, Edmonton, Canada
| | - Carin Li
- Department of Biological Sciences, University of Alberta, Edmonton, Canada
| | - David Scott Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, Canada
- Department of Computing Science, University of Alberta, Edmonton, Canada
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18
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Nemesure B, Clouston S, Albano D, Kuperberg S, Bilfinger TV. Will That Pulmonary Nodule Become Cancerous? A Risk Prediction Model for Incident Lung Cancer. Cancer Prev Res (Phila) 2019; 12:463-470. [PMID: 31248853 DOI: 10.1158/1940-6207.capr-18-0500] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 02/14/2019] [Accepted: 05/13/2019] [Indexed: 11/16/2022]
Abstract
This prospective investigation derived a prediction model for identifying risk of incident lung cancer among patients with visible lung nodules identified on computed tomography (CT). Among 2,924 eligible patients referred for evaluation of a pulmonary nodule to the Stony Brook Lung Cancer Evaluation Center between January 1, 2002 and December 31, 2015, 171 developed incident lung cancer during the observation period. Cox proportional hazard models were used to model time until disease onset. The sample was randomly divided into discovery (n = 1,469) and replication (n = 1,455) samples. In the replication sample, concordance was computed to indicate predictive accuracy and risk scores were calculated using the linear predictions. Youden index was used to identify high-risk versus low-risk patients and cumulative lung cancer incidence was examined for high-risk and low-risk groups. Multivariable analyses identified a combination of clinical and radiologic predictors for incident lung cancer including ln-age, ln-pack-years smoking, a history of cancer, chronic obstructive pulmonary disease, and several radiologic markers including spiculation, ground glass opacity, and nodule size. The final model reliably detected patients who developed lung cancer in the replication sample (C = 0.86, sensitivity/specificity = 0.73/0.81). Cumulative incidence of lung cancer was elevated in high-risk versus low-risk groups [HR = 14.34; 95% confidence interval (CI), 8.17-25.18]. Quantification of reliable risk scores has high clinical utility, enabling physicians to better stratify treatment protocols to manage patient care. The final model is among the first tools developed to predict incident lung cancer in patients presenting with a concerning pulmonary nodule.
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Affiliation(s)
- Barbara Nemesure
- Department of Family, Population and Preventive Medicine, Stony Brook Medicine, Stony Brook, New York.
| | - Sean Clouston
- Department of Family, Population and Preventive Medicine, Stony Brook Medicine, Stony Brook, New York.,Program in Public Health, Stony Brook Medicine, Stony Brook, New York
| | - Denise Albano
- Department of Surgery, Stony Brook Medicine, Stony Brook, New York
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Shi Z, Yu H, Wu Y, Lin X, Bao Q, Jia H, Perschon C, Duggan D, Helfand BT, Zheng SL, Xu J. Systematic evaluation of cancer-specific genetic risk score for 11 types of cancer in The Cancer Genome Atlas and Electronic Medical Records and Genomics cohorts. Cancer Med 2019; 8:3196-3205. [PMID: 30968590 PMCID: PMC6558466 DOI: 10.1002/cam4.2143] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 03/01/2019] [Accepted: 03/18/2019] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Genetic risk score (GRS) is an odds ratio (OR)-weighted and population-standardized method for measuring cumulative effect of multiple risk-associated single nucleotide polymorphisms (SNPs). We hypothesize that GRS is a valid tool for risk assessment of most common cancers. METHODS Utilizing genotype and phenotype data from The Cancer Genome Atlas (TCGA) and Electronic Medical Records and Genomics (eMERGE), we tested 11 cancer-specific GRSs (bladder, breast, colorectal, glioma, lung, melanoma, ovarian, pancreatic, prostate, renal, and thyroid cancer) for association with the respective cancer type. Cancer-specific GRSs were calculated, for the first time in these cohorts, based on previously published risk-associated SNPs using the Caucasian subjects in these two cohorts. RESULTS Mean cancer-specific GRS in the population controls of eMERGE approximated the expected value of 1.00 (between 0.98 and 1.02) for all 11 types of cancer. Mean cancer-specific GRS was consistently higher in respective cancer patients than controls for all 11 types of cancer (P < 0.05). When subjects were categorized into low-, average-, and high-risk groups based on cancer-specific GRS (<0.5, 0.5-1.5, and >1.5, respectively), significant dose-response associations of higher cancer-specific GRS with higher OR of respective type of cancer were found for nine types of cancer (P-trend < 0.05). More than 64% subjects in the population controls of eMERGE can be classified as high risk for at least one type of these cancers. CONCLUSION Validity of GRS for predicting cancer risk is demonstrated for most types of cancer. If confirmed in larger studies, cancer-specific GRS may have the potential for developing personalized cancer screening strategy.
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Affiliation(s)
- Zhuqing Shi
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, Illinois.,State Key Laboratory of Genetic Engineering, School of Life Science, Fudan University, Shanghai, China
| | - Hongjie Yu
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, Illinois
| | - Yishuo Wu
- Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiaoling Lin
- State Key Laboratory of Genetic Engineering, School of Life Science, Fudan University, Shanghai, China.,Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
| | - Quanwa Bao
- State Key Laboratory of Genetic Engineering, School of Life Science, Fudan University, Shanghai, China
| | - Haifei Jia
- Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
| | - Chelsea Perschon
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, Illinois
| | - David Duggan
- Translational Genomics Research Institute, Phoenix, Arizona
| | - Brian T Helfand
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, Illinois
| | - Siqun L Zheng
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, Illinois
| | - Jianfeng Xu
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, Illinois.,State Key Laboratory of Genetic Engineering, School of Life Science, Fudan University, Shanghai, China.,Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
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20
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Yu C, Qin N, Pu Z, Song C, Wang C, Chen J, Dai J, Ma H, Jiang T, Jiang Y. Integrating of genomic and transcriptomic profiles for the prognostic assessment of breast cancer. Breast Cancer Res Treat 2019; 175:691-699. [PMID: 30868394 DOI: 10.1007/s10549-019-05177-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 02/19/2019] [Indexed: 12/18/2022]
Abstract
PURPOSE To evaluate the prognostic effect of the integration of genomic and transcriptomic profiles in breast cancer. METHODS Eight hundred and ten samples from the Cancer Genome Atlas (TCGA) data sets were randomly divided into the training set (540 subjects) and validation set (270 subjects). We first selected single-nucleotide polymorphism (SNPs) and genes associated with breast cancer prognosis in the training set to construct the prognostic prediction model, and then replicated the prediction efficiency in the validation set. RESULTS Four SNPs and three genes associated with the prognosis of breast cancer in the training set were included in the prognostic model. Patients were divided into the high-risk group and low-risk group based on the four SNPs and three genes signature-based genetic prognostic index. High-risk patients showed a significant worse overall survival [Hazard Ratio (HR) 9.43, 95% confidence interval (CI) 3.81-23.33, P < 0.001] than the low-risk group. Compared to the model constructed with only gene expression, the C statistics for the signature-based genetic prognostic index [area under curves (AUC) = 0.79, 95% CI 0.72-0.86] showed a significant increase (P < 0.001). Additionally, we further replicated the prognostic prediction model in the validation set as patients in the high-risk group also showed a significantly worse overall survival (HR 4.55, 95% CI 1.50-13.88, P < 0.001), and the C statistics for the signature-based genetic prognostic index was 0.76 (95% CI 0.65-0.86). The following time-dependent ROC revealed that the mean of AUCs were 0.839 and 0.748 in the training set and the validation set, respectively. CONCLUSIONS Our findings suggested that integrating genomic and transcriptomic profiles could greatly improve the predictive efficiency of the prognosis of breast cancer patients.
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Affiliation(s)
- Chengxiao Yu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Na Qin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Zhening Pu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China.,Center of Clinical Research, Wuxi Institute of Translational Medicine, Wuxi People's Hospital of Nanjing Medical University, Wuxi, 214000, China
| | - Ci Song
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer 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.,State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China.,Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166, China
| | - Jiaping Chen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer 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.,State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Tao Jiang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China. .,State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China. .,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China.
| | - Yue Jiang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China. .,State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China. .,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China.
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Cheng YI, Davies MPA, Liu D, Li W, Field JK. Implementation planning for lung cancer screening in China. PRECISION CLINICAL MEDICINE 2019; 2:13-44. [PMID: 35694700 PMCID: PMC8985785 DOI: 10.1093/pcmedi/pbz002] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 12/19/2018] [Accepted: 12/24/2018] [Indexed: 02/05/2023] Open
Abstract
Lung cancer is the leading cause of cancer-related deaths in China, with over 690 000 lung cancer deaths estimated in 2018. The mortality has increased about five-fold from the mid-1970s to the 2000s. Lung cancer low-dose computerized tomography (LDCT) screening in smokers was shown to improve survival in the US National Lung Screening Trial, and more recently in the European NELSON trial. However, although the predominant risk factor, smoking contributes to a lower fraction of lung cancers in China than in the UK and USA. Therefore, it is necessary to establish Chinese-specific screening strategies. There have been 23 associated programmes completed or still ongoing in China since the 1980s, mainly after 2000; and one has recently been planned. Generally, their entry criteria are not smoking-stringent. Most of the Chinese programmes have reported preliminary results only, which demonstrated a different high-risk subpopulation of lung cancer in China. Evidence concerning LDCT screening implementation is based on results of randomized controlled trials outside China. LDCT screening programmes combining tobacco control would produce more benefits. Population recruitment (e.g. risk-based selection), screening protocol, nodule management and cost-effectiveness are discussed in detail. In China, the high-risk subpopulation eligible for lung cancer screening has not as yet been confirmed, as all the risk parameters have not as yet been determined. Although evidence on best practice for implementation of lung cancer screening has been accumulating in other countries, further research in China is urgently required, as China is now facing a lung cancer epidemic.
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Affiliation(s)
- Yue I Cheng
- Lung Cancer Research Group, Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool, United Kingdom
| | - Michael P A Davies
- Lung Cancer Research Group, Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool, United Kingdom
| | - Dan Liu
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - John K Field
- Lung Cancer Research Group, Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of Liverpool, William Henry Duncan Building, 6 West Derby Street, Liverpool, United Kingdom
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22
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Xin J, Chu H, Ben S, Ge Y, Shao W, Zhao Y, Wei Y, Ma G, Li S, Gu D, Zhang Z, Du M, Wang M. Evaluating the effect of multiple genetic risk score models on colorectal cancer risk prediction. Gene 2018; 673:174-180. [PMID: 29908285 DOI: 10.1016/j.gene.2018.06.035] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2018] [Revised: 05/25/2018] [Accepted: 06/12/2018] [Indexed: 12/29/2022]
Abstract
Currently, genetic risk score (GRS) model has been a widely used method to evaluate the genetic effect of cancer risk prediction, but seldom studies investigated their discriminatory power, especially for colorectal cancer (CRC) risk prediction. In this study, we applied both simulation and real data to comprehensively compare the discriminability of different GRS models. The GRS models were fitted by logistic regression with three scenarios, including simple count GRS (SC-GRS), logistic regression weighted GRS (LR-GRS, including DL-GRS and OR-GRS) and explained variance weighted GRS (EV-GRS, including EV_DL-GRS and EV_OR-GRS) models. The model performance was evaluated by receiver operating characteristic (ROC) curves and area under curves (AUC) metric, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). In real data analysis, as DL-GRS and EV_DL-GRS models were carried with serious over-fitting, the other three models were kept for further comparison. Compared to unweighted SC-GRS model, reclassification was significantly decreased in OR-GRS model (NRI = -0.082, IDI = -0.002, P < 0.05), while EV_OR-GRS model showed negative NRI and IDI (NRI = -0.077, IDI = -5.54E-04, P < 0.05) compared to OR-GRS model. Besides, traditional model with smoking status (AUC = 0.523) performed lower discriminability compared to the combined model (AUC = 0.607) including genetic (i.e., SC-GRS) and smoking factors. Similarly, the findings from simulation were all consistent to real data results. It is plausible that SC-GRS model could be optimal for predicting genetic risk of CRC. Moreover, the addition of more significant genetic variants to traditional model could further improve predictive power on CRC risk prediction.
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Affiliation(s)
- Junyi Xin
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Haiyan Chu
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Shuai Ben
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yuqiu Ge
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Wei Shao
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yang Zhao
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China; China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Yongyue Wei
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China; China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Gaoxiang Ma
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Shuwei Li
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Dongying Gu
- Department of Oncology, The Affiliated Nanjing Hospital of Nanjing Medical University, Nanjing, China
| | - Zhengdong Zhang
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China.
| | - Mulong Du
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China; China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China.
| | - Meilin Wang
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China; China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China.
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23
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Hui X, Hu Y, Sun MA, Shu X, Han R, Ge Q, Wang Y. EBT: a statistic test identifying moderate size of significant features with balanced power and precision for genome-wide rate comparisons. Bioinformatics 2018; 33:2631-2641. [PMID: 28472273 DOI: 10.1093/bioinformatics/btx294] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2016] [Accepted: 05/02/2017] [Indexed: 11/14/2022] Open
Abstract
Motivation In genome-wide rate comparison studies, there is a big challenge for effective identification of an appropriate number of significant features objectively, since traditional statistical comparisons without multi-testing correction can generate a large number of false positives while multi-testing correction tremendously decreases the statistic power. Results In this study, we proposed a new exact test based on the translation of rate comparison to two binomial distributions. With modeling and real datasets, the exact binomial test (EBT) showed an advantage in balancing the statistical precision and power, by providing an appropriate size of significant features for further studies. Both correlation analysis and bootstrapping tests demonstrated that EBT is as robust as the typical rate-comparison methods, e.g. χ 2 test, Fisher's exact test and Binomial test. Performance comparison among machine learning models with features identified by different statistical tests further demonstrated the advantage of EBT. The new test was also applied to analyze the genome-wide somatic gene mutation rate difference between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), two main lung cancer subtypes and a list of new markers were identified that could be lineage-specifically associated with carcinogenesis of LUAD and LUSC, respectively. Interestingly, three cilia genes were found selectively with high mutation rates in LUSC, possibly implying the importance of cilia dysfunction in the carcinogenesis. Availability and implementation An R package implementing EBT could be downloaded from the website freely: http://www.szu-bioinf.org/EBT . Contact wangyj@szu.edu.cn. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xinjie Hui
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Shenzhen University Health Science Center, Shenzhen 518060, China
| | - Yueming Hu
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Shenzhen University Health Science Center, Shenzhen 518060, China
| | - Ming-An Sun
- Epigenomics and Computational Biology Lab, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA 24060, USA
| | - Xingsheng Shu
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Shenzhen University Health Science Center, Shenzhen 518060, China
| | - Rongfei Han
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Shenzhen University Health Science Center, Shenzhen 518060, China
| | - Qinggang Ge
- Department of Critical Care Unit, Peking University Third Hospital, Beijing 100191, China
| | - Yejun Wang
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Shenzhen University Health Science Center, Shenzhen 518060, China
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24
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Schreuder A, Schaefer-Prokop CM, Scholten ET, Jacobs C, Prokop M, van Ginneken B. Lung cancer risk to personalise annual and biennial follow-up computed tomography screening. Thorax 2018; 73:thoraxjnl-2017-211107. [PMID: 29602813 DOI: 10.1136/thoraxjnl-2017-211107] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Revised: 03/06/2018] [Accepted: 03/12/2018] [Indexed: 11/04/2022]
Abstract
BACKGROUND All lung cancer CT screening trials used fixed follow-up intervals, which may not be optimal. We developed new lung cancer risk models for personalising screening intervals to 1 year or 2 years, and compared these with existing models. METHODS We included participants in the CT arm of the National Lung Screening Trial (2002-2010) who underwent a baseline scan and a first annual follow-up scan and were not diagnosed with lung cancer in the first year. True and false positives and the area under the curve of each model were calculated. Internal validation was performed using bootstrapping. RESULTS Data from 24 542 participants were included in the analysis. The accuracy was 0.785, 0.693, 0.697, 0.666 and 0.727 for the polynomial, patient characteristics, diameter, Patz and PanCan models, respectively. Of the 24 542 participants included, 174 (0.71%) were diagnosed with lung cancer between the first and the second annual follow-ups. Using the polynomial model, 2558 (10.4%, 95% CI 10.0% to 10.8%), 7544 (30.7%, 30.2% to 31.3%), 10 947 (44.6%, 44.0% to 45.2%), 16 710 (68.1%, 67.5% to 68.7%) and 20 023 (81.6%, 81.1% to 92.1%) of the 24 368 participants who did not develop lung cancer in the year following the first follow-up screening round could have safely skipped it, at the expense of delayed diagnosis of 0 (0.0%, 0.0% to 2.7%), 8 (4.6%, 2.2% to 9.2%), 17 (9.8%, 6.0% to 15.4%), 44 (25.3%, 19.2% to 32.5%) and 70 (40.2%, 33.0% to 47.9%) of the 174 lung cancers, respectively. CONCLUSIONS The polynomial model, using both patient characteristics and baseline scan morphology, was significantly superior in assigning participants to 1-year or 2-year screening intervals. Implementing personalised follow-up intervals would enable hundreds of participants to skip a screening round per lung cancer diagnosis delayed.
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Affiliation(s)
- Anton Schreuder
- Department of Radiology and Nuclear Medicine, Diagnostic Image Analysis Group, Radboudumc, Nijmegen, The Netherlands
| | - Cornelia M Schaefer-Prokop
- Department of Radiology and Nuclear Medicine, Diagnostic Image Analysis Group, Radboudumc, Nijmegen, The Netherlands
- Department of Radiology, Meander Medisch Centrum, Amersfoort, The Netherlands
| | - Ernst T Scholten
- Department of Radiology and Nuclear Medicine, Diagnostic Image Analysis Group, Radboudumc, Nijmegen, The Netherlands
| | - Colin Jacobs
- Department of Radiology and Nuclear Medicine, Diagnostic Image Analysis Group, Radboudumc, Nijmegen, The Netherlands
| | - Mathias Prokop
- Department of Radiology and Nuclear Medicine, Diagnostic Image Analysis Group, Radboudumc, Nijmegen, The Netherlands
| | - Bram van Ginneken
- Department of Radiology and Nuclear Medicine, Diagnostic Image Analysis Group, Radboudumc, Nijmegen, The Netherlands
- Fraunhofer MEVIS, Bremen, Germany
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25
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Abstract
Lung cancer is the leading cause of cancer deaths in both men and women in the US. While most sporadic lung cancer cases are related to environmental factors such as smoking, genetic susceptibility may also play an important role and a number of lung cancer associated single-nucleotide polymorphisms (SNPs) have been identified although many remain to be found. The collective effects of genome-wide minor alleles of common SNPs, or the minor allele content (MAC) in an individual, have been linked with quantitative variations of complex traits and diseases. Here we studied MAC in lung cancer using previously published SNPs data sets (US and Finland samples) and found higher MAC in cases relative to matched controls. A set of 5400 SNPs with MA (MAF < 0.5) more common in cases (P < 0.08) and linkage disequilibrium (LD) r2 = 0.3 was found to have the best predictive accuracy. These results identify higher MAC in lung cancer susceptibility and provide a meaningful genetic method to identify those at risk of lung cancer.
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26
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Snetselaar R, van Oosterhout MFM, Grutters JC, van Moorsel CHM. Telomerase Reverse Transcriptase Polymorphism rs2736100: A Balancing Act between Cancer and Non-Cancer Disease, a Meta-Analysis. Front Med (Lausanne) 2018. [PMID: 29536006 PMCID: PMC5835035 DOI: 10.3389/fmed.2018.00041] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
The enzyme telomerase reverse transcriptase (TERT) is essential for telomere maintenance. In replicating cells, maintenance of telomere length is important for the preservation of vital genetic information and prevention of genomic instability. A common genetic variant in TERT, rs2736100 C/A, is associated with both telomere length and multiple diseases. Carriage of the C allele is associated with longer telomere length, while carriage of the A allele is associated with shorter telomere length. Furthermore, some diseases have a positive association with the C and some with the A allele. In this study, meta-analyses were performed for two groups of diseases, cancerous diseases, e.g., lung cancer and non-cancerous diseases, e.g., pulmonary fibrosis, using data from genome-wide association studies and case-control studies. In the meta-analysis it was found that cancer positively associated with the C allele (pooled OR 1.16 [95% CI 1.09–1.23]) and non-cancerous diseases negatively associated with the C allele (pooled OR 0.81 [95% CI 0.65–0.99]). This observation illustrates that the ambiguous role of telomere maintenance in disease hinges, at least in part, on a single locus in telomerase genes. The dual role of this single nucleotide polymorphism also emphasizes that therapeutic agents aimed at influencing telomere maintenance should be used with caution.
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Affiliation(s)
- Reinier Snetselaar
- Interstitial Lung Diseases Center of Excellence, Department of Pulmonology, St Antonius Hospital, Nieuwegein, Netherlands
| | - Matthijs F M van Oosterhout
- Interstitial Lung Diseases Center of Excellence, Department of Pathology, St Antonius Hospital, Nieuwegein, Netherlands
| | - Jan C Grutters
- Interstitial Lung Diseases Center of Excellence, Department of Pulmonology, St Antonius Hospital, Nieuwegein, Netherlands.,Division of Heart and Lung, University Medical Center Utrecht, Utrecht, Netherlands
| | - Coline H M van Moorsel
- Interstitial Lung Diseases Center of Excellence, Department of Pulmonology, St Antonius Hospital, Nieuwegein, Netherlands.,Division of Heart and Lung, University Medical Center Utrecht, Utrecht, Netherlands
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27
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Tasnim T, Al-Mamun MMA, Nahid NA, Islam MR, Apu MNH, Bushra MU, Rabbi SNI, Nahar Z, Chowdhury JA, Ahmed MU, Islam MS, Hasnat A. Genetic variants of SULT1A1 and XRCC1 genes and risk of lung cancer in Bangladeshi population. Tumour Biol 2017; 39:1010428317729270. [PMID: 29110586 DOI: 10.1177/1010428317729270] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Lung cancer is one of the most frequently occurring cancers throughout the world as well as in Bangladesh. This study aimed to correlate the prognostic and/or predictive value of functional polymorphisms in SULT1A1 (rs9282861) and XRCC1 (rs25487) genes and lung cancer risk in Bangladeshi population. A case-control study was conducted which comprises 202 lung cancer patients and 242 healthy volunteers taking into account the age, sex, and smoking status. After isolation of genomic DNA, genotyping was done by polymerase chain reaction-restriction fragment length polymorphism method and the lung cancer risk was evaluated as odds ratio that was adjusted for age, sex, and smoking status. A significant association was found between SULT1A1 rs9282861 and XRCC1 rs25487 polymorphisms and lung cancer risk. In case of rs9282861 polymorphism, Arg/His (adjusted odds ratio = 5.06, 95% confidence interval = 3.05-8.41, p < 0.05) and His/His (adjusted odds ratio = 3.88, 95% confidence interval = 2.20-6.82, p < 0.05) genotypes were strongly associated with increased risk of lung cancer in comparison to the Arg/Arg genotype. In case of rs25487 polymorphism, Arg/Gln heterozygote (adjusted odds ratio = 4.57, 95% confidence interval = 2.79-7.46, p < 0.05) and Gln/Gln mutant homozygote (adjusted odds ratio = 4.99, 95% confidence interval = 2.66-9.36, p < 0.05) were also found to be significantly associated with increased risk of lung cancer. This study demonstrates that the presence of His allele and Gln allele in case of SULT1A1 rs9282861 and XRCC1 rs25487, respectively, involve in lung cancer prognosis in Bangladeshi population.
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Affiliation(s)
- Tasnova Tasnim
- 1 Department of Clinical Pharmacy and Pharmacology, Faculty of Pharmacy, University of Dhaka, Dhaka, Bangladesh.,3 Department of Pharmacy, University of Asia Pacific, Dhaka, Bangladesh
| | - Mir Md Abdullah Al-Mamun
- 1 Department of Clinical Pharmacy and Pharmacology, Faculty of Pharmacy, University of Dhaka, Dhaka, Bangladesh
| | - Noor Ahmed Nahid
- 1 Department of Clinical Pharmacy and Pharmacology, Faculty of Pharmacy, University of Dhaka, Dhaka, Bangladesh
| | - Md Reazul Islam
- 1 Department of Clinical Pharmacy and Pharmacology, Faculty of Pharmacy, University of Dhaka, Dhaka, Bangladesh
| | - Mohd Nazmul Hasan Apu
- 1 Department of Clinical Pharmacy and Pharmacology, Faculty of Pharmacy, University of Dhaka, Dhaka, Bangladesh
| | - Most Umme Bushra
- 1 Department of Clinical Pharmacy and Pharmacology, Faculty of Pharmacy, University of Dhaka, Dhaka, Bangladesh
| | | | - Zabun Nahar
- 3 Department of Pharmacy, University of Asia Pacific, Dhaka, Bangladesh
| | - Jakir Ahmed Chowdhury
- 4 Department of Pharmaceutical Technology, Faculty of Pharmacy, University of Dhaka, Dhaka, Bangladesh
| | - Maizbha Uddin Ahmed
- 1 Department of Clinical Pharmacy and Pharmacology, Faculty of Pharmacy, University of Dhaka, Dhaka, Bangladesh
| | - Mohammad Safiqul Islam
- 5 Department of Pharmacy, Noakhali Science and Technology University, Noakhali, Bangladesh
| | - Abul Hasnat
- 1 Department of Clinical Pharmacy and Pharmacology, Faculty of Pharmacy, University of Dhaka, Dhaka, Bangladesh
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Sakoda LC, Henderson LM, Caverly TJ, Wernli KJ, Katki HA. Applying Risk Prediction Models to Optimize Lung Cancer Screening: Current Knowledge, Challenges, and Future Directions. CURR EPIDEMIOL REP 2017. [PMID: 29531893 DOI: 10.1007/s40471-017-0126-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Purpose of review Risk prediction models may be useful for facilitating effective and high-quality decision-making at critical steps in the lung cancer screening process. This review provides a current overview of published lung cancer risk prediction models and their applications to lung cancer screening and highlights both challenges and strategies for improving their predictive performance and use in clinical practice. Recent findings Since the 2011 publication of the National Lung Screening Trial results, numerous prediction models have been proposed to estimate the probability of developing or dying from lung cancer or the probability that a pulmonary nodule is malignant. Respective models appear to exhibit high discriminatory accuracy in identifying individuals at highest risk of lung cancer or differentiating malignant from benign pulmonary nodules. However, validation and critical comparison of the performance of these models in independent populations are limited. Little is also known about the extent to which risk prediction models are being applied in clinical practice and influencing decision-making processes and outcomes related to lung cancer screening. Summary Current evidence is insufficient to determine which lung cancer risk prediction models are most clinically useful and how to best implement their use to optimize screening effectiveness and quality. To address these knowledge gaps, future research should be directed toward validating and enhancing existing risk prediction models for lung cancer and evaluating the application of model-based risk calculators and its corresponding impact on screening processes and outcomes.
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Affiliation(s)
- Lori C Sakoda
- Division of Research, Kaiser Permanente Northern California, Oakland, CA USA
| | - Louise M Henderson
- Department of Radiology, University of North Carolina School of Medicine, Chapel Hill, NC USA
| | - Tanner J Caverly
- Center for Clinical Management Research, Veteran Affairs Ann Arbor Healthcare System, Ann Arbor, MI USA
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI USA
| | - Karen J Wernli
- Kaiser Permanente Washington Health Research Institute, Seattle, WA USA
| | - Hormuzd A Katki
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD USA
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29
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ten Haaf K, Jeon J, Tammemägi MC, Han SS, Kong CY, Plevritis SK, Feuer EJ, de Koning HJ, Steyerberg EW, Meza R. Risk prediction models for selection of lung cancer screening candidates: A retrospective validation study. PLoS Med 2017; 14:e1002277. [PMID: 28376113 PMCID: PMC5380315 DOI: 10.1371/journal.pmed.1002277] [Citation(s) in RCA: 186] [Impact Index Per Article: 26.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2016] [Accepted: 02/27/2017] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Selection of candidates for lung cancer screening based on individual risk has been proposed as an alternative to criteria based on age and cumulative smoking exposure (pack-years). Nine previously established risk models were assessed for their ability to identify those most likely to develop or die from lung cancer. All models considered age and various aspects of smoking exposure (smoking status, smoking duration, cigarettes per day, pack-years smoked, time since smoking cessation) as risk predictors. In addition, some models considered factors such as gender, race, ethnicity, education, body mass index, chronic obstructive pulmonary disease, emphysema, personal history of cancer, personal history of pneumonia, and family history of lung cancer. METHODS AND FINDINGS Retrospective analyses were performed on 53,452 National Lung Screening Trial (NLST) participants (1,925 lung cancer cases and 884 lung cancer deaths) and 80,672 Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) ever-smoking participants (1,463 lung cancer cases and 915 lung cancer deaths). Six-year lung cancer incidence and mortality risk predictions were assessed for (1) calibration (graphically) by comparing the agreement between the predicted and the observed risks, (2) discrimination (area under the receiver operating characteristic curve [AUC]) between individuals with and without lung cancer (death), and (3) clinical usefulness (net benefit in decision curve analysis) by identifying risk thresholds at which applying risk-based eligibility would improve lung cancer screening efficacy. To further assess performance, risk model sensitivities and specificities in the PLCO were compared to those based on the NLST eligibility criteria. Calibration was satisfactory, but discrimination ranged widely (AUCs from 0.61 to 0.81). The models outperformed the NLST eligibility criteria over a substantial range of risk thresholds in decision curve analysis, with a higher sensitivity for all models and a slightly higher specificity for some models. The PLCOm2012, Bach, and Two-Stage Clonal Expansion incidence models had the best overall performance, with AUCs >0.68 in the NLST and >0.77 in the PLCO. These three models had the highest sensitivity and specificity for predicting 6-y lung cancer incidence in the PLCO chest radiography arm, with sensitivities >79.8% and specificities >62.3%. In contrast, the NLST eligibility criteria yielded a sensitivity of 71.4% and a specificity of 62.2%. Limitations of this study include the lack of identification of optimal risk thresholds, as this requires additional information on the long-term benefits (e.g., life-years gained and mortality reduction) and harms (e.g., overdiagnosis) of risk-based screening strategies using these models. In addition, information on some predictor variables included in the risk prediction models was not available. CONCLUSIONS Selection of individuals for lung cancer screening using individual risk is superior to selection criteria based on age and pack-years alone. The benefits, harms, and feasibility of implementing lung cancer screening policies based on risk prediction models should be assessed and compared with those of current recommendations.
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Affiliation(s)
- Kevin ten Haaf
- Department of Public Health, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
- * E-mail: (KtH); (RM)
| | - Jihyoun Jeon
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Martin C. Tammemägi
- Department of Health Sciences, Brock University, St. Catharines, Ontario, Canada
| | - Summer S. Han
- Department of Radiology, Stanford University, Palo Alto, California, United States of America
- Department of Medicine, Stanford University, Palo Alto, California, United States of America
| | - Chung Yin Kong
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Sylvia K. Plevritis
- Department of Radiology, Stanford University, Palo Alto, California, United States of America
| | - Eric J. Feuer
- Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland, United States of America
| | - Harry J. de Koning
- Department of Public Health, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Ewout W. Steyerberg
- Department of Public Health, Erasmus MC University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Rafael Meza
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, United States of America
- * E-mail: (KtH); (RM)
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30
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Lung Cancer Risk Prediction Using Common SNPs Located in GWAS-Identified Susceptibility Regions. J Thorac Oncol 2016; 10:1538-45. [PMID: 26352532 DOI: 10.1097/jto.0000000000000666] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
INTRODUCTION Genome-wide association studies (GWAS) have consistently identified specific lung cancer susceptibility regions. We evaluated the lung cancer-predictive performance of single-nucleotide polymorphisms (SNPs) in these regions. METHODS Lung cancer cases (N = 778) and controls (N = 1166) were genotyped for 77 SNPs located in GWAS-identified lung cancer susceptibility regions. Variable selection and model development used stepwise logistic regression and decision-tree analyses. In a subset nested in the Pittsburgh Lung Screening Study, change in area under the receiver operator characteristic curve and net reclassification improvement were used to compare predictions made by risk factor models with and without genetic variables. RESULTS Variable selection and model development kept two SNPs in each of three GWAS regions, rs2736100 and rs7727912 in 5p15.33, rs805297 and rs1802127 in 6p21.33, and rs8034191 and rs12440014 in 15q25.1. The ratio of cases to controls was three times higher among subjects with a high-risk genotype in every one as opposed to none of the three GWAS regions (odds ratio, 3.14; 95% confidence interval, 2.02-4.88; adjusted for sex, age, and pack-years). Adding a three-level classified count of GWAS regions with high-risk genotypes to an age and smoking risk factor-only model improved lung cancer prediction by a small amount: area under the receiver operator characteristic curve, 0.725 versus 0.717 (p = 0.056); overall net reclassification improvement was 0.052 across low-, intermediate-, and high- 6-year lung cancer risk categories (<3.0%, 3.0%-4.9%, ≥ 5.0%). CONCLUSION Specifying genotypes for SNPs in three GWAS-identified susceptibility regions improved lung cancer prediction, but probably by an extent too small to affect disease control practice.
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31
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Apperley S, Lam S. Region specific lung nodule management practice guideline. J Thorac Dis 2016; 8:2319-2323. [PMID: 27746965 PMCID: PMC5059281 DOI: 10.21037/jtd.2016.09.31] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2016] [Accepted: 07/15/2016] [Indexed: 12/21/2022]
Affiliation(s)
- Scott Apperley
- Department of Respiratory Medicine, University of British Columbia, Vancouver, Canada
- Department of Integrative Oncology, British Columbia Cancer Research Center, Vancouver, Canada
| | - Stephen Lam
- Department of Respiratory Medicine, University of British Columbia, Vancouver, Canada
- Department of Integrative Oncology, British Columbia Cancer Research Center, Vancouver, Canada
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Zhao Y, Chen G, Yu H, Hu L, Bian Y, Yun D, Chen J, Mao Y, Chen H, Lu D. Development of risk prediction models for glioma based on genome-wide association study findings and comprehensive evaluation of predictive performances. Oncotarget 2016; 9:8311-8325. [PMID: 29492197 PMCID: PMC5823595 DOI: 10.18632/oncotarget.10882] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2015] [Accepted: 06/29/2016] [Indexed: 12/17/2022] Open
Abstract
Over 14 common single nucleotide polymorphisms (SNP) have been consistently identified from genome-wide association studies (GWAS) as associated with glioma risk in European background. The extent to which and how these genetic variants can improve the prediction of glioma risk has was not been investigated. In this study, we employed three independent case-control datasets in Chinese populations, tested GWAS signals in dataset1, validated association results in dataset2, developed prediction models in dataset2 for the consistently replicated SNPs, refined the consistently replicated SNPs in dataset3 and developed tailored models for Chinese populations. For model construction, we aggregated the contribution of multiple SNPs into genetic risk scores (count GRS and weighed GRS) or predicted risks from logistic regression analyses (PRFLR). In dataset2, the area under receiver operating characteristic curves (AUC) of the 5 consistently replicated SNPs by PRFLR(SNPs) was 0.615, higher than those of all GRSs(ranging from 0.607 to 0.611, all P>0.05). The AUC of genetic profile significantly exceeded that of family history (fmc) alone (AUC=0.535, all P<0.001). The best model in our study comprised “PRURA +fmc” (AUC=0.646) in dataset3. Further model assessment analyses provided additional evidence. This study indicates that genetic markers have potential value for risk prediction of glioma.
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Affiliation(s)
- Yingjie Zhao
- State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Gong Chen
- Neurosurgery Department of Huashan Hospital, Fudan University, Shanghai, China
| | - Hongjie Yu
- State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China.,Center for Genetic Epidemiology, School of Life Sciences, Fudan University, Shanghai, China
| | - Lingna Hu
- State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Yunmeng Bian
- State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Dapeng Yun
- State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Juxiang Chen
- Department of Neurosurgery, Changzheng Hospital, Second Military Medical University, Shanghai, China
| | - Ying Mao
- Neurosurgery Department of Huashan Hospital, Fudan University, Shanghai, China
| | - Hongyan Chen
- State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China
| | - Daru Lu
- State Key Laboratory of Genetic Engineering and MOE Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Institute of Genetics, School of Life Sciences, Fudan University, Shanghai, China
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Cheng Y, Jiang T, Zhu M, Li Z, Zhang J, Wang Y, Geng L, Liu J, Shen W, Wang C, Hu Z, Jin G, Ma H, Shen H, Dai J. Risk assessment models for genetic risk predictors of lung cancer using two-stage replication for Asian and European populations. Oncotarget 2016; 8:53959-53967. [PMID: 28903315 PMCID: PMC5589554 DOI: 10.18632/oncotarget.10403] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Accepted: 06/04/2016] [Indexed: 11/25/2022] Open
Abstract
In the past ten years, great successes have been accumulated by taking advantage of both candidate-gene studies and genome-wide association studies. However, limited studies were available to systematically evaluate the genetic effects for lung cancer risk with large-scale and different ethnic populations. We systematically reviewed relevant literatures and filtered out 241 important genetic variants identified in 124 articles. A two-stage case-control study within specific subgroups was performed to assess the effects [Training set: 2,331 cases vs. 3,077 controls (Chinese population); testing set: 1,937 cases vs. 1,984 controls (European population)]. Variable selection and model development were used LASSO penalized regression and genetic risk score (GRS) system. Further change in area under the receiver operator characteristic curves (AUC) made by the epidemiologic model with and without GRS was used to compare predictions. It kept 38 genetic variants in our study and the ratios of lung cancer risk for subjects in the upper quartile GRS was three times higher compared to that in the low quartile (odds ratio: 4.64, 95% CI: 3.87–5.56). In addition, we found that adding genetic predictors to smoking risk factor-only model improved lung cancer predictive value greatly: AUC, 0.610 versus 0.697 (P < 0.001). Similar performance was derived in European population and the combined two data sets. Our findings suggested that genetic predictors could improve the predictive ability of risk model for lung cancer and highlighted the application among different populations, indicating that the lung cancer risk assessment model will be a promising tool for high risk population screening and prediction.
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Affiliation(s)
- Yang Cheng
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Tao Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Meng Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Zhihua Li
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Jiahui Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Yuzhuo Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Liguo Geng
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Jia Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Wei Shen
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Cheng Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China
| | - Zhibin Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Guangfu Jin
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Hongxia Ma
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Hongbing Shen
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Juncheng Dai
- Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.,Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer Medicine, Nanjing Medical University, Nanjing, 211166, China
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Qian DC, Han Y, Byun J, Shin HR, Hung RJ, McLaughlin JR, Landi MT, Seminara D, Amos CI. A Novel Pathway-Based Approach Improves Lung Cancer Risk Prediction Using Germline Genetic Variations. Cancer Epidemiol Biomarkers Prev 2016; 25:1208-15. [PMID: 27222311 DOI: 10.1158/1055-9965.epi-15-1318] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2015] [Accepted: 05/13/2016] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Although genome-wide association studies (GWAS) have identified many genetic variants that are strongly associated with lung cancer, these variants have low penetrance and serve as poor predictors of lung cancer in individuals. We sought to increase the predictive value of germline variants by considering their cumulative effects in the context of biologic pathways. METHODS For individuals in the Environment and Genetics in Lung Cancer Etiology study (1,815 cases/1,971 controls), we computed pathway-level susceptibility effects as the sum of relevant SNP variant alleles weighted by their log-additive effects from a separate lung cancer GWAS meta-analysis (7,766 cases/37,482 controls). Logistic regression models based on age, sex, smoking, genetic variants, and principal components of pathway effects and pathway-smoking interactions were trained and optimized in cross-validation and further tested on an independent dataset (556 cases/830 controls). We assessed prediction performance using area under the receiver operating characteristic curve (AUC). RESULTS Compared with typical binomial prediction models that have epidemiologic predictors (AUC = 0.607) in addition to top GWAS variants (AUC = 0.617), our pathway-based smoking-interactive multinomial model significantly improved prediction performance in external validation (AUC = 0.656, P < 0.0001). CONCLUSIONS Our biologically informed approach demonstrated a larger increase in AUC over nongenetic counterpart models relative to previous approaches that incorporate variants. IMPACT This model is the first of its kind to evaluate lung cancer prediction using subtype-stratified genetic effects organized into pathways and interacted with smoking. We propose pathway-exposure interactions as a potentially powerful new contributor to risk inference. Cancer Epidemiol Biomarkers Prev; 25(8); 1208-15. ©2016 AACR.
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Affiliation(s)
- David C Qian
- Department of Biomedical Data Science, Dartmouth Geisel School of Medicine, Lebanon, New Hampshire
| | - Younghun Han
- Department of Biomedical Data Science, Dartmouth Geisel School of Medicine, Lebanon, New Hampshire
| | - Jinyoung Byun
- Department of Biomedical Data Science, Dartmouth Geisel School of Medicine, Lebanon, New Hampshire
| | - Hae Ri Shin
- Department of Biomedical Data Science, Dartmouth Geisel School of Medicine, Lebanon, New Hampshire
| | - Rayjean J Hung
- Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, Canada
| | - John R McLaughlin
- Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | | | | | - Christopher I Amos
- Department of Biomedical Data Science, Dartmouth Geisel School of Medicine, Lebanon, New Hampshire.
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Wang X, Ma K, Chi L, Cui J, Jin L, Hu JF, Li W. Combining Telomerase Reverse Transcriptase Genetic Variant rs2736100 with Epidemiologic Factors in the Prediction of Lung Cancer Susceptibility. J Cancer 2016; 7:846-53. [PMID: 27162544 PMCID: PMC4860802 DOI: 10.7150/jca.13437] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2015] [Accepted: 03/15/2016] [Indexed: 01/01/2023] Open
Abstract
Genetic variants from a considerable number of susceptibility loci have been identified in association with cancer risk, but their interaction with epidemiologic factors in lung cancer remains to be defined. We sought to establish a forecasting model for identifying individuals with high-risk of lung cancer by combing gene single-nucleotide polymorphisms with epidemiologic factors. Genotyping and clinical data from 500 lung cancer cases and 500 controls were used for developing the logistic regression model. We found that lung cancer was associated with telomerase reverse transcriptase (TERT) rs2736100 single-nucleotide polymorphism. The TERT rs2736100 model was still significantly associated with lung cancer risk when combined with environmental and lifestyle factors, including lower education, lower BMI, COPD history, heavy cigarettes smoking, heavy cooking emission, and dietary factors (over-consumption of meat and deficiency in fish/shrimp, vegetables, dairy products, and soybean products). These data suggest that combining TERT SNP and epidemiologic factors may be a useful approach to discriminate high and low-risk individuals for lung cancer.
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Affiliation(s)
- Xu Wang
- 1. Cancer and Stem Cell Center, First Affiliated Hospital, Jilin University, Changchun, Jilin 130061, P.R. China.; 2. Stanford University Medical School Stanford, Palo Alto Veterans Institute for Research, Palo Alto, CA94305, USA
| | - Kewei Ma
- 1. Cancer and Stem Cell Center, First Affiliated Hospital, Jilin University, Changchun, Jilin 130061, P.R. China
| | - Lumei Chi
- 4. School of Public Health, Jilin University, Changchun 130021, Jilin, P. R. China
| | - Jiuwei Cui
- 1. Cancer and Stem Cell Center, First Affiliated Hospital, Jilin University, Changchun, Jilin 130061, P.R. China
| | - Lina Jin
- 3. Second Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun , Jilin 130033, P.R. China
| | - Ji-Fan Hu
- 1. Cancer and Stem Cell Center, First Affiliated Hospital, Jilin University, Changchun, Jilin 130061, P.R. China.; 2. Stanford University Medical School Stanford, Palo Alto Veterans Institute for Research, Palo Alto, CA94305, USA
| | - Wei Li
- 1. Cancer and Stem Cell Center, First Affiliated Hospital, Jilin University, Changchun, Jilin 130061, P.R. China
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Marcus MW, Raji OY, Duffy SW, Young RP, Hopkins RJ, Field JK. Incorporating epistasis interaction of genetic susceptibility single nucleotide polymorphisms in a lung cancer risk prediction model. Int J Oncol 2016; 49:361-70. [PMID: 27121382 PMCID: PMC4902078 DOI: 10.3892/ijo.2016.3499] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2016] [Accepted: 02/17/2016] [Indexed: 02/06/2023] Open
Abstract
Incorporation of genetic variants such as single nucleotide polymorphisms (SNPs) into risk prediction models may account for a substantial fraction of attributable disease risk. Genetic data, from 2385 subjects recruited into the Liverpool Lung Project (LLP) between 2000 and 2008, consisting of 20 SNPs independently validated in a candidate-gene discovery study was used. Multifactor dimensionality reduction (MDR) and random forest (RF) were used to explore evidence of epistasis among 20 replicated SNPs. Multivariable logistic regression was used to identify similar risk predictors for lung cancer in the LLP risk model for the epidemiological model and extended model with SNPs. Both models were internally validated using the bootstrap method and model performance was assessed using area under the curve (AUC) and net reclassification improvement (NRI). Using MDR and RF, the overall best classifier of lung cancer status were SNPs rs1799732 (DRD2), rs5744256 (IL-18), rs2306022 (ITGA11) with training accuracy of 0.6592 and a testing accuracy of 0.6572 and a cross-validation consistency of 10/10 with permutation testing P<0.0001. The apparent AUC of the epidemiological model was 0.75 (95% CI 0.73–0.77). When epistatic data were incorporated in the extended model, the AUC increased to 0.81 (95% CI 0.79–0.83) which corresponds to 8% increase in AUC (DeLong's test P=2.2e-16); 17.5% by NRI. After correction for optimism, the AUC was 0.73 for the epidemiological model and 0.79 for the extended model. Our results showed modest improvement in lung cancer risk prediction when the SNP epistasis factor was added.
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Affiliation(s)
- Michael W Marcus
- Roy Castle Lung Cancer Research Programme, The University of Liverpool, Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, Liverpool L7 8TX, UK
| | - Olaide Y Raji
- Roy Castle Lung Cancer Research Programme, The University of Liverpool, Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, Liverpool L7 8TX, UK
| | - Stephen W Duffy
- Wolfson Institute of Preventive Medicine, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
| | - Robert P Young
- School of Biological Sciences, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Raewyn J Hopkins
- School of Biological Sciences, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - John K Field
- Roy Castle Lung Cancer Research Programme, The University of Liverpool, Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, Liverpool L7 8TX, UK
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Polymorphisms in human telomerase reverse transcriptase (hTERT) gene and susceptibility to gastric cancer in a Turkish population: Hospital-based case-control study. Gene 2016; 585:84-92. [PMID: 27016301 DOI: 10.1016/j.gene.2016.03.030] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2016] [Accepted: 03/19/2016] [Indexed: 12/19/2022]
Abstract
Erosion of telomeres, tandem nucleotide repeats (TTAGGG)n that cap the end of eukaryotic chromosomes, has been related with carcinogenesis. The human telomerase reverse transcriptase (hTERT) gene is encoded the rate-limiting catalytic subunit of the telomerase complexes, which is essential for the protection of telomeric DNA length and chromosomal stability. The purpose of this study was to examine the effect of four functional single nucleotide polymorphisms (SNPs) of hTERT (rs2736109 G>A, rs2735940 T>C, rs2853669 A>G and rs2736100 T>G) on susceptibility to gastric cancer (GC) in Turkish population. The genotype frequency of hTERT rs2736109 G>A, rs2735940 T>C, rs2853669 A>G and rs2736100 T>G polymorphisms were determined by using a polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) and TaqMan methods in 104 subjects with GC and 209 healthy control subjects. We found that hTERT rs2736109 G>A (AA+AG vs. GG OR=1.68 95% CI=1.01-2.81, P=0.04), rs2735940 T>C (CC vs. CT+TT: OR=2.53 95% CI=1.01-6.13, P=0.03), and rs2736100 T>G (TT vs. TG+GG: OR=2.27 95% CI=1.23-4.17, P=0.006) polymorphisms were associated with risk of GC. In the haplotype analysis, hTERT Grs2736109/Trs2735940/Ars2853669/Grs2736100 haplotype was also related with an increased risk of GC (OR=1.75; 95% CI: 1.05-2.93, P=0.03). Because this is the first study regarding the hTERT rs2736109 G>A, rs2735940 T>C, rs2853669 A>G and rs2736100 T>G polymorphisms and the risk of GC susceptibility in the literature, further independent studies are needed to verify our results in a larger sample sizes, as well as in patients of different populations.
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Potenciano V, Abad-Grau MM, Alcina A, Matesanz F. A comparison of genomic profiles of complex diseases under different models. BMC Med Genomics 2016; 9:3. [PMID: 26782991 PMCID: PMC4717655 DOI: 10.1186/s12920-015-0157-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2014] [Accepted: 11/27/2015] [Indexed: 12/15/2022] Open
Abstract
Background Various approaches are being used to predict individual risk to polygenic diseases from data provided by genome-wide association studies. As there are substantial differences between the diseases investigated, the data sets used and the way they are tested, it is difficult to assess which models are more suitable for this task. Results We compared different approaches for seven complex diseases provided by the Wellcome Trust Case Control Consortium (WTCCC) under a within-study validation approach. Risk models were inferred using a variety of learning machines and assumptions about the underlying genetic model, including a haplotype-based approach with different haplotype lengths and different thresholds in association levels to choose loci as part of the predictive model. In accordance with previous work, our results generally showed low accuracy considering disease heritability and population prevalence. However, the boosting algorithm returned a predictive area under the ROC curve (AUC) of 0.8805 for Type 1 diabetes (T1D) and 0.8087 for rheumatoid arthritis, both clearly over the AUC obtained by other approaches and over 0.75, which is the minimum required for a disease to be successfully tested on a sample at risk, which means that boosting is a promising approach. Its good performance seems to be related to its robustness to redundant data, as in the case of genome-wide data sets due to linkage disequilibrium. Conclusions In view of our results, the boosting approach may be suitable for modeling individual predisposition to Type 1 diabetes and rheumatoid arthritis based on genome-wide data and should be considered for more in-depth research. Electronic supplementary material The online version of this article (doi:10.1186/s12920-015-0157-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Víctor Potenciano
- Departamento de Lenguajes y Sistemas Informáticos, ETSIIT, c/ Periodista Daniel Saucedo Aranda s/n Universidad de Granada, Granada, 18071, Spain.
| | - María Mar Abad-Grau
- Departamento de Lenguajes y Sistemas Informáticos, ETSIIT, c/ Periodista Daniel Saucedo Aranda s/n Universidad de Granada, Granada, 18071, Spain.
| | - Antonio Alcina
- Instituto de Parasitología y Biología Molecular, CSIC, Granada, Spain.
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Schwartz AG, Cote ML. Epidemiology of Lung Cancer. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2016; 893:21-41. [PMID: 26667337 DOI: 10.1007/978-3-319-24223-1_2] [Citation(s) in RCA: 122] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Lung cancer continues to be one of the most common causes of cancer death despite understanding the major cause of the disease: cigarette smoking. Smoking increases lung cancer risk 5- to 10-fold with a clear dose-response relationship. Exposure to environmental tobacco smoke among nonsmokers increases lung cancer risk about 20%. Risks for marijuana and hookah use, and the new e-cigarettes, are yet to be consistently defined and will be important areas for continued research as use of these products increases. Other known environmental risk factors include exposures to radon, asbestos, diesel, and ionizing radiation. Host factors have also been associated with lung cancer risk, including family history of lung cancer, history of chronic obstructive pulmonary disease and infections. Studies to identify genes associated with lung cancer susceptibility have consistently identified chromosomal regions on 15q25, 6p21 and 5p15 associated with lung cancer risk. Risk prediction models for lung cancer typically include age, sex, cigarette smoking intensity and/or duration, medical history, and occupational exposures, however there is not yet a risk prediction model currently recommended for general use. As lung cancer screening becomes more widespread, a validated model will be needed to better define risk groups to inform screening guidelines.
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Affiliation(s)
- Ann G Schwartz
- Department of Oncology, Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI, USA.
| | - Michele L Cote
- Department of Oncology, Karmanos Cancer Institute, Wayne State University School of Medicine, Detroit, MI, USA
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40
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Gray EP, Teare MD, Stevens J, Archer R. Risk Prediction Models for Lung Cancer: A Systematic Review. Clin Lung Cancer 2015; 17:95-106. [PMID: 26712102 DOI: 10.1016/j.cllc.2015.11.007] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Revised: 11/09/2015] [Accepted: 11/12/2015] [Indexed: 11/25/2022]
Abstract
Many lung cancer risk prediction models have been published but there has been no systematic review or comprehensive assessment of these models to assess how they could be used in screening. We performed a systematic review of lung cancer prediction models and identified 31 articles that related to 25 distinct models, of which 11 considered epidemiological factors only and did not require a clinical input. Another 11 articles focused on models that required a clinical assessment such as a blood test or scan, and 8 articles considered the 2-stage clonal expansion model. More of the epidemiological models had been externally validated than the more recent clinical assessment models. There was varying discrimination, the ability of a model to distinguish between cases and controls, with an area under the curve between 0.57 and 0.879 and calibration, the model's ability to assign an accurate probability to an individual. In our review we found that further validation studies need to be considered; especially for the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial 2012 Model Version (PLCOM2012) and Hoggart models, which recorded the best overall performance. Future studies will need to focus on prediction rules, such as optimal risk thresholds, for models for selective screening trials. Only 3 validation studies considered prediction rules when validating the models and overall the models were validated using varied tests in distinct populations, which made direct comparisons difficult. To improve this, multiple models need to be tested on the same data set with considerations for sensitivity, specificity, model accuracy, and positive predictive values at the optimal risk thresholds.
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Affiliation(s)
- Eoin P Gray
- Department of School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom.
| | - M Dawn Teare
- Department of School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom
| | - John Stevens
- Department of School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom
| | - Rachel Archer
- Department of School of Health and Related Research, University of Sheffield, Sheffield, United Kingdom
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Guo S, Yan F, Xu J, Bao Y, Zhu J, Wang X, Wu J, Li Y, Pu W, Liu Y, Jiang Z, Ma Y, Chen X, Xiong M, Jin L, Wang J. Identification and validation of the methylation biomarkers of non-small cell lung cancer (NSCLC). Clin Epigenetics 2015; 7:3. [PMID: 25657825 PMCID: PMC4318209 DOI: 10.1186/s13148-014-0035-3] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2014] [Accepted: 12/10/2014] [Indexed: 11/26/2022] Open
Abstract
Background DNA methylation was suggested as the promising biomarker for lung cancer diagnosis. However, it is a great challenge to search for the optimal combination of methylation biomarkers to obtain maximum diagnostic performance. Results In this study, we developed a panel of DNA methylation biomarkers and validated their diagnostic efficiency for non-small cell lung cancer (NSCLC) in a large Chinese Han NSCLC retrospective cohort. Three high-throughput DNA methylation microarray datasets (458 samples) were collected in the discovery stage. After normalization, batch effect elimination and integration, significantly differentially methylated genes and the best combination of the biomarkers were determined by the leave-one-out SVM (support vector machine) feature selection procedure. Then, candidate promoters were examined by the methylation status determined single nucleotide primer extension technique (MSD-SNuPET) in an independent set of 150 pairwise NSCLC/normal tissues. Four statistical models with fivefold cross-validation were used to evaluate the performance of the discriminatory algorithms. The sensitivity, specificity and accuracy were 86.3%, 95.7% and 91%, respectively, in Bayes tree model. The logistic regression model incorporated five gene methylation signatures at AGTR1, GALR1, SLC5A8, ZMYND10 and NTSR1, adjusted for age, sex and smoking, showed robust performances in which the sensitivity, specificity, accuracy, and area under the curve (AUC) were 78%, 97%, 87%, and 0.91, respectively. Conclusions In summary, a high-throughput DNA methylation microarray dataset followed by batch effect elimination can be a good strategy to discover optimal DNA methylation diagnostic panels. Methylation profiles of AGTR1, GALR1, SLC5A8, ZMYND10 and NTSR1, could be an effective methylation-based assay for NSCLC diagnosis. Electronic supplementary material The online version of this article (doi:10.1186/s13148-014-0035-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Shicheng Guo
- State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University Jiangwan Campus, 2005 Songhu Road, Shanghai, 200438 China ; Fudan-Taizhou Institute of Health Sciences, 1 Yaocheng Road, Taizhou, Jiangsu 225300 China
| | - Fengyang Yan
- State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University Jiangwan Campus, 2005 Songhu Road, Shanghai, 200438 China
| | - Jibin Xu
- Department of Cardiothoracic Surgery, Changzheng Hospital of Shanghai, Fengyang Road 415, Shanghai, 200000 China
| | - Yang Bao
- Yangzhou No.1 People's Hospital, 368 Hanjiang Road, Yangzhou, 225001 China
| | - Ji Zhu
- Department of Cardiothoracic Surgery, Changhai Hospital of Shanghai, Changhai Road 168, Shanghai, 200433 China
| | - Xiaotian Wang
- State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University Jiangwan Campus, 2005 Songhu Road, Shanghai, 200438 China
| | - Junjie Wu
- State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University Jiangwan Campus, 2005 Songhu Road, Shanghai, 200438 China ; Department of Pneumology, Changhai Hospital of Shanghai, Changhai Road 168, Shanghai, 200433 China
| | - Yi Li
- State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University Jiangwan Campus, 2005 Songhu Road, Shanghai, 200438 China
| | - Weilin Pu
- State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University Jiangwan Campus, 2005 Songhu Road, Shanghai, 200438 China
| | - Yan Liu
- Center for Genetic & Genomic Analysis, Genesky Biotechnologies Inc., 787 Kangqiao Road, Shanghai, 201203 China
| | - Zhengwen Jiang
- Center for Genetic & Genomic Analysis, Genesky Biotechnologies Inc., 787 Kangqiao Road, Shanghai, 201203 China
| | - Yanyun Ma
- State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University Jiangwan Campus, 2005 Songhu Road, Shanghai, 200438 China
| | - Xiaofeng Chen
- Department of Cardiothoracic Surgery, Huashan Hospital, Fudan University, 12 Wulumuqi Road, Shanghai, 200040 China
| | - Momiao Xiong
- Human Genetics Center, The University of Texas School of Public Health, 1200 Herman Pressler, Houston, Texas 77030 USA
| | - Li Jin
- State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University Jiangwan Campus, 2005 Songhu Road, Shanghai, 200438 China ; Fudan-Taizhou Institute of Health Sciences, 1 Yaocheng Road, Taizhou, Jiangsu 225300 China
| | - Jiucun Wang
- State Key Laboratory of Genetic Engineering and Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University Jiangwan Campus, 2005 Songhu Road, Shanghai, 200438 China ; Fudan-Taizhou Institute of Health Sciences, 1 Yaocheng Road, Taizhou, Jiangsu 225300 China
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Abstract
OBJECTIVE Avoidable mortality is a well-recognized, but less studied indicator of the performance of the health system. First, the study seeks to establish whether immigrants overall and selected foreign-born ethnic groups (Western Europeans, South Asians, Chinese, and Filipinos) have an advantage over nonimmigrants in avoidable mortality. Second, it assesses the effect of sociodemographic and socioeconomic factors on any observed differences by duration of residence. DESIGN Deaths grouped by cause of death and by behavioral risk factors, namely smoking-related and alcohol-related, were derived from the 1991 Canadian Census Cohort: Mortality and Cancer Follow-up. The analysis estimated age-standardized mortality rates (ASMRs), rate ratios, and rate differences and also fitted hazard regression models for the overall Canadian-born population and for selected foreign-born ethnicities by sex. Predictors were assessed at baseline. RESULTS Compared to the Canadian-born persons, foreign-born men and women had lower ASMRs for overall avoidable mortality and also for selected causes of avoidable mortality. The only exception to this overall trend was for ischemic heart disease among South Asian women. Except for the order of prominence, the three leading causes of death for nonimmigrant and immigrant men and women overall were ischemic heart diseases, smoking-related diseases, and neoplasms. A similar pattern was observed among the ethnic groups, except for circulatory heart diseases replacing ischemic heart diseases and smoking-related diseases among Chinese and Filipino women, respectively. In the hazard regression analysis, the risk of avoidable mortality was lower for immigrants overall and selected ethnicities irrespective of the duration in Canada compared to nonimmigrants. These differences persisted even with adjustment for sociodemographic and socioeconomic factors. CONCLUSION Immigrants overall and the selected ethnicities enjoy an advantage over nonimmigrants in avoidable mortality. However, for certain causes of death especially ischemic heart disease mortality among South Asian women, immigrants appeared worse-off than nonimmigrants. The results suggest differential access to and use of health services, differences in protective health-related behavior, and the healthy immigrant effect.
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Malhotra J. Molecular and Genetic Epidemiology of Cancer in Low- and Medium-Income
Countries. Ann Glob Health 2014; 80:418-25. [DOI: 10.1016/j.aogh.2014.09.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
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Wang X, Oldani MJ, Zhao X, Huang X, Qian D. A review of cancer risk prediction models with genetic variants. Cancer Inform 2014; 13:19-28. [PMID: 25288876 PMCID: PMC4179686 DOI: 10.4137/cin.s13788] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2014] [Revised: 06/30/2014] [Accepted: 07/01/2014] [Indexed: 12/31/2022] Open
Abstract
Cancer risk prediction models are important in identifying individuals at high risk of developing cancer, which could result in targeted screening and interventions to maximize the treatment benefit and minimize the burden of cancer. The cancer-associated genetic variants identified in genome-wide or candidate gene association studies have been shown to collectively enhance cancer risk prediction, improve our understanding of carcinogenesis, and possibly result in the development of targeted treatments for patients. In this article, we review the cancer risk prediction models that have been developed for popular cancers and assess their applicability, strengths, and weaknesses. We also discuss the factors to be considered for future development and improvement of models for cancer risk prediction.
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Affiliation(s)
- Xuexia Wang
- Joseph J. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
| | - Michael J Oldani
- Criminology and Anthropology Department, University of Wisconsin-Whitewater, Whitewater, WI, USA
| | - Xingwang Zhao
- Joseph J. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
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Nie W, Zang Y, Chen J, Xiu Q. TERT rs2736100 polymorphism contributes to lung cancer risk: a meta-analysis including 49,869 cases and 73,464 controls. Tumour Biol 2014; 35:5569-74. [PMID: 24535778 DOI: 10.1007/s13277-014-1734-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2013] [Accepted: 02/05/2014] [Indexed: 11/27/2022] Open
Abstract
Some studies investigated the association of TERT rs2736100 polymorphism with lung cancer (LC). But the results were not consistent. We performed a meta-analysis to examine the association between rs2736100 and LC. Databases including PubMed, EMBASE, Wanfang, and China National Knowledge Infrastructure (CNKI) were searched. Data were extracted, and pooled odds ratios (ORs) with 95 % confidence intervals (CIs) were calculated. A total of 19 studies including 49,869 cases and 73,464 controls were involved in this meta-analysis. Overall, a significant association between TERT rs2736100 polymorphism and LC risk was observed (OR=1.23, 95 % CI 1.18-1.28, P<0.00001). This polymorphism was also significantly associated with LC risk in Asians (OR=1.27, 95 % CI 1.22-1.33, P<0.00001), Caucasians (OR=1.14, 95 % CI 1.10-1.18, P<0.00001), female patients (OR=1.37, 95 % CI 1.24-1.51, P<0.00001), male patients (OR=1.23, 95 % CI 1.15-1.31, P<0.00001), adenocarcinoma patients (OR=1.35, 95 % CI 1.28-1.41, P<0.00001), squamous cell carcinoma patients (OR=1.13, 95 % CI 1.04-1.21, P=0.002), small cell lung cancer patients (OR=1.09, 95 % CI 1.03-1.16, P=0.004), current smokers (OR=1.22, 95 % CI 1.17-1.27, P<0.00001), former smokers (OR=1.14, 95 % CI 1.08-1.21, P<0.0001), and never smokers (OR=1.37, 95 % CI 1.31-1.43, P<0.00001), respectively. This meta-analysis suggested that TERT rs2736100 polymorphism was a risk factor for LC.
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Affiliation(s)
- Wei Nie
- Department of Respiratory Medicine, Shanghai Changzheng Hospital, Second Military Medical University, 415 Fengyang Road, Shanghai, 200003, China
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