Molecular Subtyping of Gastric Cancer and Prognostic Biomarker Identification Based on Multi-Omics Analysis

Gastric cancer exhibits remarkable molecular heterogeneity, which poses significant challenges for accurate prognosis and personalized treatment. To address this issue, this study performs a comprehensive multi-omics analysis integrating genomics, transcriptomics, and proteomics data to establish robust molecular subtypes of gastric cancer and to identify reliable prognostic biomarkers. Using data from large-scale public databases and clinical cohorts, unsupervised clustering methods were applied to construct molecular subtypes with distinct biological characteristics and clinical outcomes. Functional enrichment and pathway analyses were conducted to elucidate the underlying molecular mechanisms of each subtype. Furthermore, machine learning–based models were developed to screen and validate key prognostic biomarkers associated with overall survival and disease progression. The results reveal multiple stable molecular subtypes with significant differences in immune microenvironment, metabolic pathways, and therapeutic sensitivity. Several hub genes and protein signatures were identified as independent prognostic factors and were validated across external datasets. These biomarkers demonstrate strong predictive performance for patient survival and risk stratification. This study provides a systematic framework for molecular classification of gastric cancer based on multi-omics integration and offers novel prognostic biomarkers for clinical application.