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

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.

Keywords: Gastric cancer; Macrophage; Nomogram; Risk model.

Fig. 1

Identification of DEGs in the TCGA-GC datasets. a Volcano plot showing DEGs between GC and normal tissues. Red dots represent upregulated genes, green dots represent downregulated genes, and gray dots indicate non-significant genes. b Cluster dendrogram showing the grouping of genes into modules based on expression similarities using WGCNA. Different colors represent different gene modules. c Univariate Cox regression analysis of the differential expression of CXCR6, CXCL3, CXCR4, and ACKR3 on the survival of GC patients. d Multivariate Cox regression analysis of CXCR6, CXCR3, CXCR4, and ACKR3. e Kaplan-Meier survival curve showing significant differences in overall survival between cluster 1 and cluster 2. f Volcano plot illustrating the DEGs between cluster 1 and cluster 2. Red dots represent upregulated genes, and green dots represent downregulated genes. 3.3 Totally 3,364 DEGs 2 were identified

Fig. 2

Single-cell RNA sequencing analysis focusing on DEGs in gastric cancer macrophages. a UMAP clustering plot showing 37 cell clusters identified from single-cell data, with different colors representing distinct clusters. b UMAP plot showing the 12 annotated cell subtypes, including T cells, macrophages, epithelial cells, and fibroblasts. Different colors represent distinct cell types. c Volcano plot showing DEGs in macrophages. Red dots indicate upregulated genes, and green dots indicate downregulated genes. d Venn diagram showing the overlap of genes from WGCNA, subtype, and macrophage differential expression analyses

Fig. 3

Survival analysis for GC patients based on the expression of key prognostic genes. a Univariate Cox regression analysis of candidate genes. b LASSO regression analysis for the selection of prognostic genes. The coefficient profiles (top) and the partial likelihood deviance (bottom) were plotted against log lambda. The optimal lambda value was chosen based on cross-validation. c Risk score distribution and survival status of GC patients in the training cohort. Patients were classified into high-risk and low-risk groups based on the median risk score. Survival status is shown below, with blue representing alive and red representing deceased. d Heatmap displaying the expression levels of the selected prognostic genes (GPX3, SERPINE1, and SPARC) in the training cohort. Red indicates high expression, and blue indicates low expression. e Kaplan-Meier survival curves for GC patients in the training cohort. f Time-dependent ROC curves for the risk model in the training cohort, assessing the model’s predictive accuracy at 1, 3, and 5 years. g Risk score distribution and survival status of gastric cancer patients in the validation cohort. h Kaplan-Meier survival curves for the validation cohort. i Time-dependent ROC curves for the validation cohort, assessing the model’s predictive performance at 1, 3, and 5 years

Fig. 4

Univariate and multivariate Cox regression analyses, and the construction of a nomogram to predict the 1-year, 3-year, and 5-year survival probabilities for GC patients. a Univariate Cox regression analysis and PH assumption test results showing the impact of risk score, age, differentiation and T/N/M staging on the prognosis of GC patients. b Multivariate Cox regression analysis results, identifying age, N stage, and M stage as independent prognostic factors for GC patients. c Nomogram illustrating the impact of risk score, age, N stage, and M stage on survival prediction for GC patients. d Calibration curve for the nomogram showing the agreement between predicted and actual survival probabilities at 1 year, 3 years, and 5 years. e ROC curve for the nomogram displaying the AUC values for 1-year, 3-year, and 5-year survival predictions

Fig. 5

Correlation analysis between infiltration cells and prognosis genes. a Relative abundance of 22 immune cell types in GC and normal control samples. Different colors represent different immune cell types. b Violin plot depicting the significant differences in immune cell types between GC and normal control samples. c Correlation heatmap between immune infiltration cells and prognostic genes. d Violin plot comparing stromal score, immune score, and ESTIMATE score between high-risk and low-risk groups

Fig. 6

Expression of prognostic gene (GPX3, SERPINE1, SPARC) in various cell subpopulations, cell communication networks, and macrophage differentiation trajectories in GC. a–c Violin plots representing the expression levels of GPX3(a), SERPINE1(b) and SPARC(c) in various cell subpopulations. Red indicates the GC group, and green represents the normal group. d Cell communication network analysis of different cell subpopulations in GC tissue. e Pseudotime analysis showing the differentiation trajectories of macrophages in GC and normal tissues. f Expression of GPX3, SERPINE1, and SPARC along the pseudotime trajectory. The panel shows the relationship between gene expression and pseudotime