ArticleCancers2021
Risk Prediction for Gastric Cancer Using GWAS-Identifie Polymorphisms,
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Genome wide association analysis and association of genetic risk and lifestyle with upper gastrointestinal cancer among an endoscopy-screened cohort in China.Science bulletin · 2026Article
- Construction and interpretation of machine learning-based prognostic models for survival prediction among intestinal-type and diffuse-type gastric cancer patients.World journal of surgical oncology · 2024Article
- Evaluation of polygenic risk score for risk prediction of gastric cancer.World journal of gastrointestinal oncology · 2023Review
- An Immune Signature for Risk Stratification and Therapeutic Prediction in Helicobacter pylori-Infected Gastric Cancer.Cancers · 2022Article
- A Model for Gastric Cancer Risk Prediction Based onIn vivo (Athens, Greece)Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
backgroundAs part of our efforts to develop practical intervention applications for cancer prevention, we investigated a risk prediction model for gastric cancer based on genetic, biological, and lifestyle-related risk factors.
methodsWe conducted two independent age- and sex-matched case-control studies, the first for model derivation (696 cases and 1392 controls) and the second (795 and 795) for external validation. Using the derivation study data, we developed a prediction model by fitting a conditional logistic regression model using the predictors age, ABCD classification defined by
resultsA combination of selected GWAS-identified polymorphisms and the other predictors provided high discriminatory accuracy and good calibration in both the derivation and validation studies, with AUCs of 0.77 (95% confidence intervals: 0.75-0.79) and 0.78 (0.77-0.81), respectively. The calibration plots of both studies stayed close to the ideal calibration line. In the validation study, the environmental model (nongenetic model) was significantly more discriminative than the inclusive model, with an AUC value of 0.80 (0.77-0.82).
conclusionThe contribution of genetic factors to risk prediction was limited, and the ABCD classification (
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.