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Sun C, Cheng X, Xu J, Chen H, Tao J, Dong Y, Wei S, Chen R, Meng X, Ma Y, Tian H, Guo X, Bi S, Zhang C, Kang J, Zhang M, Lv H, Shang Z, Lv W, Zhang R, Jiang Y. A review of disease risk prediction methods and applications in the omics era. Proteomics 2024:e2300359. [PMID: 38522029 DOI: 10.1002/pmic.202300359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 03/08/2024] [Accepted: 03/12/2024] [Indexed: 03/25/2024]
Abstract
Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.
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Affiliation(s)
- Chen Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Xiangshu Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Jing Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Haiyan Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Junxian Tao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Yu Dong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Siyu Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Rui Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xin Meng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yingnan Ma
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Hongsheng Tian
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xuying Guo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shuo Bi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chen Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jingxuan Kang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Mingming Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hongchao Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zhenwei Shang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Wenhua Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Ruijie Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yongshuai Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
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Maciel-Cruz EJ, Figuera-Villanueva LE, Garibaldi-Ríos AF, Gómez-Meda BC, Zúñiga-González GM, Pérez AM, Castro-García PB, Ramírez-Patiño R, Gallegos-Arreola MP. AURKA Gene Variants rs1047972, and rs8173 Are Associated With Breast Cancer. J Breast Cancer 2023; 26:378-390. [PMID: 37565930 PMCID: PMC10475707 DOI: 10.4048/jbc.2023.26.e31] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 04/28/2023] [Accepted: 06/06/2023] [Indexed: 08/12/2023] Open
Abstract
PURPOSE Association between variants rs1047972 and rs8173 of the AURKA gene in healthy women and breast cancer (BC) in a Mexican population. METHODS Genomic DNA samples from 409 healthy women and 572 patients with BC were analyzed for variants rs1047972 and rs8173 of the AURKA gene by polymerase chain reaction-restriction fragment length polymorphism. RESULTS TT genotype (odds ratio [OR], 2.5; 95% confidence interval [CI], 1.22-5.11; p = 0.0015) and the T allele (OR, 1.16; 95% CI, 1.23-2.12; p = 0.0007) of the rs1047972 variant were associated as risk susceptibility for BC relative to the control group. Contrarily, the GG genotype (OR, 0.64; 95% CI, 0.43-0.94; p = 0.029) was associated as a protective factor of susceptibility of BC of the variant rs8173 of the AURKA gene. Differences were observed in the patients with BC who were carriers of the CT genotype of the rs1047972 variant with overweight, obesity, estrogen receptor-positive plus obesity, Ki-67 (≥ 20%) plus history familial positive of cancer; and for variant rs8173 the BC patients who were CG carriers and presented chemotherapy gastric toxicity, hormonal receptor positive plus chemotherapy gastric toxicity, and menopause status plus chemotherapy gastric toxicity (p < 0.05). Two common haplotypes were identified in the study groups: CG and TC genotypes, were associated as a protective and risk factor, respectively (p < 0.05). CONCLUSION Variants rs1047972 and rs8173 of the AURKA gene and the TC haplotype were associated as risk susceptibility factors for BC in this population.
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Affiliation(s)
- Eric Jonathan Maciel-Cruz
- División de Genética, Centro de Investigación Biomédica de Occidente (CIBO), Centro Médico Nacional de Occidente (CMNO), Instituto Mexicano del Seguro Social (IMSS), Guadalajara, México
- Doctorado en Genética Humana, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara (UdeG), Guadalajara, México
| | - Luis Eduardo Figuera-Villanueva
- División de Genética, Centro de Investigación Biomédica de Occidente (CIBO), Centro Médico Nacional de Occidente (CMNO), Instituto Mexicano del Seguro Social (IMSS), Guadalajara, México
- Doctorado en Genética Humana, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara (UdeG), Guadalajara, México
| | - Asbiel Felipe Garibaldi-Ríos
- División de Genética, Centro de Investigación Biomédica de Occidente (CIBO), Centro Médico Nacional de Occidente (CMNO), Instituto Mexicano del Seguro Social (IMSS), Guadalajara, México
- Doctorado en Genética Humana, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara (UdeG), Guadalajara, México
| | - Belinda Claudia Gómez-Meda
- Departamento de Biología Molecular y Genómica, Instituto de Genética Humana "Dr. Enrique Corona Rivera", Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara (UdeG), Guadalajara, México
| | - Guillermo Moisés Zúñiga-González
- División de Medicina Molecular, Centro de Investigación Biomédica de Occidente (CIBO), Centro Médico Nacional de Occidente (CMNO), Instituto Mexicano del Seguro Social (IMSS), Guadalajara, México
| | - Ana María Pérez
- Laboratorio de Inmunofarmacología, Centro Universitario de Ciencias Exactas e Ingenierías Universidad de Guadalajara (UdeG), Guadalajara, México
| | - Paola B Castro-García
- Laboratorio de Inmunofarmacología, Centro Universitario de Ciencias Exactas e Ingenierías Universidad de Guadalajara (UdeG), Guadalajara, México
| | - Ramiro Ramírez-Patiño
- Departamento de Medicina y Ciencias de la Vida, Centro Universitario la Ciénega, Universidad de Guadalajara (UdeG), Ocotlán, México
| | - Martha Patricia Gallegos-Arreola
- División de Genética, Centro de Investigación Biomédica de Occidente (CIBO), Centro Médico Nacional de Occidente (CMNO), Instituto Mexicano del Seguro Social (IMSS), Guadalajara, México.
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Mertens E, Barrenechea-Pulache A, Sagastume D, Vasquez MS, Vandevijvere S, Peñalvo JL. Understanding the contribution of lifestyle in breast cancer risk prediction: a systematic review of models applicable to Europe. BMC Cancer 2023; 23:687. [PMID: 37480028 PMCID: PMC10360320 DOI: 10.1186/s12885-023-11174-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 07/12/2023] [Indexed: 07/23/2023] Open
Abstract
BACKGROUND Breast cancer (BC) is a significant health concern among European women, with the highest prevalence rates among all cancers. Existing BC prediction models account for major risks such as hereditary, hormonal and reproductive factors, but research suggests that adherence to a healthy lifestyle can reduce the risk of developing BC to some extent. Understanding the influence and predictive role of lifestyle variables in current risk prediction models could help identify actionable, modifiable, targets among high-risk population groups. PURPOSE To systematically review population-based BC risk prediction models applicable to European populations and identify lifestyle predictors and their corresponding parameter values for a better understanding of their relative contribution to the prediction of incident BC. METHODS A systematic review was conducted in PubMed, Embase and Web of Science from January 2000 to August 2021. Risk prediction models were included if (i) developed and/or validated in adult cancer-free women in Europe, (ii) based on easily ascertained information, and (iii) reported models' final predictors. To investigate further the comparability of lifestyle predictors across models, estimates were standardised into risk ratios and visualised using forest plots. RESULTS From a total of 49 studies, 33 models were developed and 22 different existing models, mostly from Gail (22 studies) and Tyrer-Cuzick and co-workers (12 studies) were validated or modified for European populations. Family history of BC was the most frequently included predictor (31 models), while body mass index (BMI) and alcohol consumption (26 and 21 models, respectively) were the lifestyle predictors most often included, followed by smoking and physical activity (7 and 6 models respectively). Overall, for lifestyle predictors, their modest predictive contribution was greater for riskier lifestyle levels, though highly variable model estimates across different models. CONCLUSIONS Given the increasing BC incidence rates in Europe, risk models utilising readily available risk factors could greatly aid in widening the population coverage of screening efforts, while the addition of lifestyle factors could help improving model performance and serve as intervention targets of prevention programmes.
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Affiliation(s)
- Elly Mertens
- Unit of Non-Communicable Diseases, Department of Public Health, Institute of Tropical Medicine, Nationalestraat 155, 2000, Antwerp, Belgium.
| | - Antonio Barrenechea-Pulache
- Unit of Non-Communicable Diseases, Department of Public Health, Institute of Tropical Medicine, Nationalestraat 155, 2000, Antwerp, Belgium
| | - Diana Sagastume
- Unit of Non-Communicable Diseases, Department of Public Health, Institute of Tropical Medicine, Nationalestraat 155, 2000, Antwerp, Belgium
| | - Maria Salve Vasquez
- Health Information, Scientific Institute of Public Health (Sciensano), Brussels, Belgium
| | - Stefanie Vandevijvere
- Health Information, Scientific Institute of Public Health (Sciensano), Brussels, Belgium
| | - José L Peñalvo
- Unit of Non-Communicable Diseases, Department of Public Health, Institute of Tropical Medicine, Nationalestraat 155, 2000, Antwerp, Belgium
- Global Health Institute, University of Antwerp, Antwerp, Belgium
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Wang XY, Wang LL, Xu L, Liang SZ, Yu MC, Zhang QY, Dong QJ. Evaluation of polygenic risk score for risk prediction of gastric cancer. World J Gastrointest Oncol 2023; 15:276-285. [PMID: 36908320 PMCID: PMC9994049 DOI: 10.4251/wjgo.v15.i2.276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 01/11/2023] [Accepted: 02/02/2023] [Indexed: 02/14/2023] Open
Abstract
Genetic variations are associated with individual susceptibility to gastric cancer. Recently, polygenic risk score (PRS) models have been established based on genetic variants to predict the risk of gastric cancer. To assess the accuracy of current PRS models in the risk prediction, a systematic review was conducted. A total of eight eligible studies consisted of 544842 participants were included for evaluation of the performance of PRS models. The overall accuracy was moderate with Area under the curve values ranging from 0.5600 to 0.7823. Incorporation of epidemiological factors or Helicobacter pylori (H. pylori) status increased the accuracy for risk prediction, while selection of single nucleotide polymorphism (SNP) and number of SNPs appeared to have little impact on the model performance. To further improve the accuracy of PRS models for risk prediction of gastric cancer, we summarized the association between gastric cancer risk and H. pylori genomic variations, cancer associated bacteria members in the gastric microbiome, discussed the potentials for performance improvement of PRS models with these microbial factors. Future studies on comprehensive PRS models established with human SNPs, epidemiological factors and microbial factors are indicated.
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Affiliation(s)
- Xiao-Yu Wang
- Central Laboratories and Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao University, Qingdao 266071, Shandong Province, China
| | - Li-Li Wang
- Central Laboratories and Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao University, Qingdao 266071, Shandong Province, China
| | - Lin Xu
- Central Laboratories and Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao University, Qingdao 266071, Shandong Province, China
| | - Shu-Zhen Liang
- Central Laboratories and Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao University, Qingdao 266071, Shandong Province, China
| | - Meng-Chao Yu
- Central Laboratories and Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao University, Qingdao 266071, Shandong Province, China
| | - Qiu-Yue Zhang
- Department of Clinical Laboratory, the Eighth Medical Center of the General Hospital of the People’s Liberation Army, Beijing 100000, China
| | - Quan-Jiang Dong
- Central Laboratories and Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao University, Qingdao 266071, Shandong Province, China
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Wang XY, Wang LL, Liang SZ, Yang C, Xu L, Yu MC, Wang YX, Dong QJ. Prediction of gastric cancer risk by a polygenic risk score of Helicobacter pylori. World J Gastrointest Oncol 2022; 14:1844-1855. [PMID: 36187384 PMCID: PMC9516638 DOI: 10.4251/wjgo.v14.i9.1844] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 04/29/2022] [Accepted: 08/16/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Genetic variants of Helicobacter pylori (H. pylori) are involved in gastric cancer occurrence. Single nucleotide polymorphisms (SNPs) of H. pylori that are associated with gastric cancer have been reported. The combined effect of H. pylori SNPs on the risk of gastric cancer remains unclear.
AIM To assess the performance of a polygenic risk score (PRS) based on H. pylori SNPs in predicting the risk of gastric cancer.
METHODS A total of 15 gastric cancer-associated H. pylori SNPs were selected. The associations between these SNPs and gastric cancer were further validated in 1022 global strains with publicly available genome sequences. The PRS model was established based on the validated SNPs. The performance of the PRS for predicting the risk of gastric cancer was assessed in global strains using quintiles and random forest (RF) methods. The variation in the performance of the PRS among different populations of H. pylori was further examined.
RESULTS Analyses of the association between selected SNPs and gastric cancer in the global dataset revealed that the risk allele frequencies of six SNPs were significantly higher in gastric cancer cases than non-gastric cancer cases. The PRS model constructed subsequently with these validated SNPs produced significantly higher scores in gastric cancer. The odds ratio (OR) value for gastric cancer gradually increased from the first to the fifth quintile of PRS, with the fifth quintile having an OR value as high as 9.76 (95% confidence interval: 5.84-16.29). The results of RF analyses indicated that the area under the curve (AUC) value for classifying gastric cancer and non-gastric cancer was 0.75, suggesting that the PRS based on H. pylori SNPs was capable of predicting the risk of gastric cancer. Assessing the performance of the PRS among different H. pylori populations demonstrated that it had good predictive power for cancer risk for hpEurope strains, with an AUC value of 0.78.
CONCLUSION The PRS model based on H. pylori SNPs had a good performance for assessment of gastric cancer risk. It would be useful in the prediction of final consequences of the H. pylori infection and beneficial for the management of the infection in clinical settings.
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Affiliation(s)
- Xiao-Yu Wang
- Central Laboratories and Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao University, Qingdao 266071, Shandong Province, China
| | - Li-Li Wang
- Central Laboratories and Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao University, Qingdao 266071, Shandong Province, China
| | - Shu-Zhen Liang
- Central Laboratories and Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao University, Qingdao 266071, Shandong Province, China
| | - Chao Yang
- The Center for Microbes, Development and Health, CAS Key Laboratory of Molecular Virology and Immunology, Institute Pasteur of Shanghai, Chinese Academy of Sciences, Shanghai 200000, China
| | - Lin Xu
- Central Laboratories and Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao University, Qingdao 266071, Shandong Province, China
| | - Meng-Chao Yu
- Central Laboratories and Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao University, Qingdao 266071, Shandong Province, China
| | - Yi-Xuan Wang
- Central Laboratories and Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao University, Qingdao 266071, Shandong Province, China
| | - Quan-Jiang Dong
- Central Laboratories and Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao University, Qingdao 266071, Shandong Province, China
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