Background  & Objectives
Gastric cancer (GC) represents a substantial global health challenge, ranking as the fifth most common malignancy and the fourth leading cause of cancer-related mortality worldwide, with approximately 968,000 new cases and 660,000 deaths reported in 2022. Despite improvements in prevention and advancement in treatment, projections indicate a 23% increase in European GC cases between 2022 and 2040, with substantial regional heterogeneity. Polygenic risk scores (PRS), which aggregate effects of multiple genetic variants identified through genome-wide association studies (GWAS), have emerged as promising tools for cancer risk stratification, demonstrating improved predictive accuracy when integrated with traditional risk factors. However, their application to GC, particularly in European populations, remains limited, representing a critical gap in personalized prevention strategies. To address this gap, our study aims to develop and validate a robust PRS for GC. We will evaluate the utility of the PRS in predicting GC risk within a broad European cohort and assess its added value to traditional risk models. By integrating genetic data into risk prediction, this work seeks to advance personalized prevention and early detection strategies for GC.
 
Methods 
The study encompasses approximately 8,247 GC cases and 353,694 controls with GWAS data, alongside comprehensive demographic, lifestyle, and clinical covariates. The development phase utilized around 6,350 cases and 14,963 controls from Helsinki Biobank, Rotterdam Study, StoP Consortium (Italy, Spain, Poland, Sweden, France, Portugal), and additional cohorts from Germany and Estonia. Independent validation employed around 1,900 cases and 339,231 controls from distinct StoP Consortium subsets (Rome, Latvia, Lithuania) and UK Biobank. GC-associated SNPs were identified and synthesized through inverse-variance-weighted fixed-effects meta-analysis. Two PRS models were constructed: Standard Clumping and Thresholding, which applies p-value and linkage disequilibrium (LD) clumping across multiple significance thresholds, and LDpred2, a Bayesian approach that incorporates genome-wide LD information for effect size estimation. The model exhibiting superior predictive performance was selected. Finally, the discriminatory accuracy of the optimal PRS was evaluated by integrating it with solely traditional risk factors (demographic, environmental, and lifestyle variables) in multivariable logistic regression models. 
 
Results 
Preliminary analyses revealed distinct distributions of established risk factors between cases and controls, consistent with known gastric cancer epidemiology. Gastric cancer cases exhibited skewed distributions of modifiable risk factors versus controls, including higher alcohol intake (11.2% vs. 8.8), increased current smoking prevalence (29.1% vs. 25.2%), and greater family history among first-degree relatives (8.6). Genome-wide meta-analysis across cohorts identified 37,136,654 markers selected based on predefined significance thresholds. SNPs with the highest effect sizes mapped predominantly to chromosomes 1, 2, 4, 8, 9, and 17. Two PRS models are currently under development using these GWAS summary statistics. 
 
Conclusions  
A validated polygenic risk score (PRS) for gastric cancer represents a major advance in personalized risk stratification by integrating cumulative genetic susceptibility with established environmental and lifestyle factors. Its development in diverse European cohorts will support equitable, data-driven prevention and early detection strategies, addressing the growing burden of gastric cancer and enabling more targeted interventions.