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<p>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 (<i>n</i> = 350 tumors, 31 controls) and GEO datasets (GSE84437, <i>n</i> = 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, <i>p</i> &lt; 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.</p>

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Bioinformatics analysis of macrophage-associated genes reveals prognostic signatures and immune landscape in gastric cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月20日

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  • Rongbo Han,
  • Fei Wang,
  • Xiujuan Wang,
  • Benxin Zhao

摘要

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.