Development and validation of a gene signature for gastric cancer prognosis based on propionate metabolism.

Background: Gastric cancer (GC), the fifth most common cancer worldwide, has high morbidity and mortality rates. Propionate metabolism, which plays a significant role in cancer progression, remains understudied in the context of GC development and progression. This study aimed to identify propanoate metabolism-related genes (PMRGs) with prognostic value, construct a predictive model for GC outcomes, and explore their associations with tumor microenvironment (TME) and therapeutic response. Methods: The data of GC patients included RNA-sequencing expression profiles, the clinical data, and mutation data gained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Data from GC patients in TCGA and GEO were used to develop and validate a prognostic model through univariate Cox and least absolute shrinkage and selection operator (LASSO) regression, by selecting key differentially expressed PMRGs (DE-PMRGs). Patients were divided into high- and low-risk groups by median risk scores. The prognostic model was evaluated by the Kaplan-Meier (K-M) curve and time-dependent receiver operating characteristic (ROC). A nomogram was created using PMRGs to predict 1-, 2-, and 3-year overall survival (OS), which was further validated with decision curve analysis (DCA) and calibration curves. Results: There were 63 DE-PMRGs were identified, with eight key hub PMRGs selected by LASSO and Cox regression for a prognostic model that predicted better survival in the low-risk group. A nomogram was constructed to predict the 1, 2 and 3 years survival rates of GC patients. In the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of high-risk groups, DE-PMRGs are primarily enriched in pathways related to muscle cells and cardiac diseases. Additionally, five immune cell types showed disparities between high and low-risk groups. Immune infiltration analysis suggested a higher potential for immune therapy response in the low-risk cohorts, and drug sensitivity prediction in GC indicates a broader sensitivity to chemotherapy drugs in the high-risk groups. Conclusions: We developed a prognostic model based on PMRGs to forecast clinical outcomes in GC patients independently. The model was refined to identify eight key genes, serving as a tool for prognosis and treatment planning in GC.