Predictive model for risk of gastric cancer using genetic variants from genome‐wide association studies and high‐evidence meta‐analysis

Autor: Jiucun Wang, Weijian Guo, Ruoxin Zhang, Jing He, Yajun Yang, Mengyun Wang, Li-Xin Qiu, Xiaofei Qu, Xiao-Dong Zhu, Menghong Sun, Lei Cheng
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Male
0301 basic medicine
Oncology
Cancer Research
Genome-wide association study
Logistic regression
susceptibility
predictive model
0302 clinical medicine
Risk Factors
immune system diseases
skin and connective tissue diseases
Original Research
genome‐wide association study
Middle Aged
lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Phenotype
030220 oncology & carcinogenesis
Meta-analysis
Female
Cancer Prevention
China
medicine.medical_specialty
Single-nucleotide polymorphism
Biology
Polymorphism
Single Nucleotide
Risk Assessment
lcsh:RC254-282
03 medical and health sciences
Meta-Analysis as Topic
Predictive Value of Tests
Stomach Neoplasms
Internal medicine
Biomarkers
Tumor
medicine
Humans
SNP
Genetic Predisposition to Disease
Radiology
Nuclear Medicine and imaging
Genetic Testing
Retrospective Studies
Genetic association
Models
Genetic
Multifactor dimensionality reduction
Receiver operating characteristic
gastric cancer
Reproducibility of Results
030104 developmental biology
Gene-Environment Interaction
prognosis
Genome-Wide Association Study
Zdroj: Cancer Medicine, Vol 9, Iss 19, Pp 7310-7316 (2020)
Cancer Medicine
ISSN: 2045-7634
Popis: Genome‐wide association studies (GWAS) have identified some single nucleotide polymorphisms (SNPs) associated with the risk of gastric cancer (GCa). However, currently, there is no published predictive model to assess the risk of GCa. In the present study, risk‐associated SNPs derived from GWAS and large meta‐analyses were selected to construct a predictive model to assess the risk of GCa. A total of 1115 GCa cases and 1172 controls from the eastern Chinese population were included. Logistic regression models were used to identify SNPs that correlated with the risk of GCa. A predictive model to assess the risk of GCa was established by receiver operating characteristic curve analysis. Multifactor dimensionality reduction (MDR) and classification and regression tree (CART) were applied to calculate the effect of high‐order gene‐environment interactions on risk of the cancer. A total of 42 SNPs were selected for further analysis. The results revealed that ASH1L rs80142782, PKLR rs3762272, PRKAA1 rs13361707, MUC1 rs4072037, PSCA rs2294008, and PLCE1 rs2274223 polymorphisms were associated with a risk of GCa. The area under curve considering both genetic factors and BMI was 3.10% higher than that of BMI alone. MDR analysis revealed that rs13361707 and rs4072307 variants and BMI had interaction effects on susceptibility to GCa, with the highest predictive accuracy (61.23%) and cross‐validation consistency (100/100). CART analysis also supported this interaction model that non‐overweight status and a six SNP panel could synergistically increase the susceptibility to GCa. The six SNP panel for predicting the risk of GCa may provide new tools for prevention of the cancer based on GWAS and large meta‐analyses derived genetic variants.
We identifed a six‐SNPs panel from GWAS and large meta‐analysis for predicting risk of gastric cancer, which may provide new tools for gastric cancer prevention.
Databáze: OpenAIRE
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