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Background  Gastric cancer is one of the most common malignancies worldwide. Although the diagnosis and treatment of this disease have substantially improved in recent years, the five-year survival rate of gastric cancer is still low due to local recurrence and distant metastasis. An in-depth study of the molecular pathogenesis of gastric cancer and related prognostic markers will help improve the quality of life and prognosis of patients with this disease. The purpose of this study was to identify and verify key SNPs in genes with prognostic value for gastric cancer.    Methods  SNP-related data from gastric cancer patients were obtained from The Cancer Genome Atlas (TCGA) database, and the functions and pathways of the mutated genes were analyzed using DAVID software. A protein-protein interaction (PPI) network was constructed using the STRING database and visualized by Cytoscape software, and molecular complex detection (MCODE) was used to screen the PPI network to extract important mutated genes. Ten hub genes were identified using cytoHubba, and the expression levels and the prognostic value of the central genes were determined by UALCAN and Kaplan-Meier Plotter. Finally, quantitative PCR and Western blotting were used to verify the expression of the hub genes in gastric cancer cells.    Results  From the database, 945 genes with mutations in more than 25 samples were identified. The PPI network had 360 nodes and 1616 edges. Finally, cytoHubba identified six key genes (TP53, HRAS, BRCA1, PIK3CA, AKT1, and SMARCA4), and their expression levels were closely related to the survival rate of gastric cancer patients.    Conclusion  Our results indicate that TP53, HRAS, BRCA1, PIK3CA, AKT1, and SMARCA4 may be key genes for the development and prognosis of gastric cancer. Our research provides an important bioinformatics foundation and related theoretical foundation for further exploring the molecular pathogenesis of gastric cancer and evaluating the prognosis of patients.

Identification and Validation of SNP-Containing Genes With Prognostic Value in Gastric Cancer via Integrated Bioinformatics Analysis