Exploration of m7G-related immune genes and construction of a prognostic risk model in gastric cancer

Gastric cancer (GC) is a lethal digestive malignancy with poor outcomes. Although N7-Methylguanosine (m7G) modification and immune-related genes (IRGs) individually affect GC prognosis and the tumor microenvironment, their mechanistic crosstalk remains unclear. Differentially expressed genes (DEGs) in GC were identified and used alongside m7G-related genes (m7G-RGs) to perform consensus clustering, defining molecular subtypes and yielding additional DEGs. Intersection of these gene sets identified m7G-related immune genes. Prognostic genes were selected via Least Absolute Shrinkage and Selection Operator (LASSO), and Cox regression analysis to construct a validated prognostic risk model. Associations between clinicopathological features and risk scores were assessed, identifying independent prognostic factors and enabling nomogram development. The risk-stratified groups underwent gene set enrichment analysis (GSEA), immune infiltration, tumor immune dysfunction and exclusion (TIDE), tumor purity, drug sensitivity, and tumor mutation burden (TMB) analyses. Using GC and normal samples, this study identified 4,458 DEGs. Intersection with subtype-specific DEGs (2,098) and immune genes (4,172) yielded 193 m7G-associated immune genes. Six prognostic genes (ELANE, ASCL2, APOA1, GRP, CD36, MUC15) were used to construct a prognostic risk model that stratified patients into high- and low-risk groups with significant survival differences and strong predictive accuracy. Age, stage, and risk score were independent prognostic factors, and a nomogram was developed. High-risk patients showed enrichment in tumor-promoting pathways (e.g., calcium signaling), immunosuppressive microenvironments, higher stromal scores, and TP53 mutations. Low-risk patients had activated DNA repair pathways, lower TIDE scores, higher immune infiltration, and TTN mutations. Drug sensitivity analysis identified 130 compounds with differential responses between groups (p < 0.05). This study developed a robust prognostic risk model based on six prognostic genes, which effectively reveals molecular mechanisms and immune features of GC, offering a foundation for individualized therapy.