Abstract
Gastric cancer (GC) remains one of the leading causes of cancer-related mortality worldwide. The interaction between macrophages and the tumor immune microenvironment (TME) plays a critical role in disease progression and patient prognosis. In this study, we conducted a comprehensive bioinformatics analysis to identify macrophage-associated prognostic genes and construct a predictive risk model in GC. Using transcriptome data from TCGA (n = 350 tumors, 31 controls) and GEO datasets (GSE84437, n = 483; GSE183904), we applied differential expression analysis (DESeq2), weighted gene co-expression network analysis (WGCNA), single-cell RNA sequencing (Seurat), and Cox-LASSO regression to screen for key prognostic markers. Three genes-GPX3, SERPINE1, and SPARC-were identified and used to build a risk score model. Patients were stratified into high- and low-risk groups. Kaplan-Meier analysis showed significantly shorter survival in the high-risk group (HR = 2.35, p < 0.001). The model achieved strong predictive performance with area under the curve (AUC) values of 0.73, 0.70, and 0.68 at 1, 3, and 5 years, respectively. Immune infiltration analysis using CIBERSORT revealed that GPX3 and SPARC were positively correlated with plasma cells and negatively with M0 macrophages. A nomogram incorporating risk score, age, and N/M stage further improved prognostic accuracy. Drug sensitivity analysis (pRRophetic) identified 27 compounds with differential predicted IC50 values between risk groups.Our study demonstrates that macrophage-associated gene signatures are robust predictors of GC prognosis. These findings provide novel insights into immune regulation and potential therapeutic targets in gastric cancer.