Prediction of gastric cancer risk by a polygenic risk score of .

Background: Genetic variants of () are involved in gastric cancer occurrence. Single nucleotide polymorphisms (SNPs) of that are associated with gastric cancer have been reported. The combined effect of SNPs on the risk of gastric cancer remains unclear.

Aim: To assess the performance of a polygenic risk score (PRS) based on SNPs in predicting the risk of gastric cancer.

Methods: A total of 15 gastric cancer-associated 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 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 SNPs was capable of predicting the risk of gastric cancer. Assessing the performance of the PRS among different 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 SNPs had a good performance for assessment of gastric cancer risk. It would be useful in the prediction of final consequences of the infection and beneficial for the management of the infection in clinical settings.