Identification of senescence-related genes as diagnostic biomarkers for gastric cancer using bioinformatics and machine learning

Gastric cancer (GC) presents a significant global health challenge with a poor prognosis due to late detection. This study combines single-cell RNA sequencing and bulk transcriptomics to identify senescence-related gastric cancer genes (SGCGs) as diagnostic biomarkers for GC. Using weighted gene co-expression network analysis (WGCNA) and machine learning-based feature selection, we identified 20 core SAGs enriched in mitochondrial and cell cycle pathways. An RF-XGBoost ensemble model achieved high predictive accuracy (ROC = 0.841), with PNPT1 emerging as a key driver through SHAP analysis. Experimental validation confirmed overexpression of PNPT1 in GC cells, with its expression correlating with age-related progression. A web-based Shiny app was developed to support clinical risk stratification. These findings highlight the importance of SGCGs in GC development and offer a translational tool for early detection and personalized treatment.