Metabolic-immune interactions in gastric cancer T cells: A single-cell atlas for prognostic biomarker identification

Metabolic alterations and immune dysfunction within the gastric tumor microenvironment critically drive gastric cancer (GC) progression and therapeutic resistance. Although single-cell RNA sequencing (scRNA-seq) has unveiled cellular heterogeneity in GC, the metabolic landscapes of tumor cells and their interplay with immune components remain underexplored. By integrating scRNA-seq data from 35,633 cells across 23 GC tissues (GSE150290), bulk RNA-seq data from UCSC Xena, and two independent microarray cohorts (GSE26899, GSE62254), we systematically characterized metabolic heterogeneity and identified immune-related prognostic biomarkers. Reclustering of malignant epithelial cells revealed distinct metabolic phenotypes, with the citrate cycle and oxidative phosphorylation pathways emerging as key drivers of intratumoral diversity and T cell differentiation. Through machine learning and survival analyses, we discovered a novel risk score model composed of 6 T cell differentiation signatures, which stratified patients into high- and low-risk groups with significant differences in overall survival. Notably, this model outperformed traditional clinicopathological factors in predicting prognosis, validated in both bulk RNA-seq and microarray datasets. Immunohistochemistry further confirmed the prognostic value of key regulatory proteins (RGS1, CXCR4, CTLA4, ARPP19, ZNRF1, and ZNF207). Our findings highlight the metabolic immune crosstalk in GC and provide a promising biomarker panel for precision risk stratification and potential immunotherapeutic targets.