Integrative Multiomics Analysis Reveals Tumor-Associated Macrophage Heterogeneity and a Prognostic Signature in...

Gastric cancer (GC) is characterized by a complex tumor microenvironment (TME) with substantial cellular heterogeneity. Tumor-associated macrophages (TAMs) represent the most abundant immune cell population in the TME and exhibit remarkable functional plasticity. This study integrated single-cell RNA-sequencing (scRNA-seq) data, bulk transcriptomics, and spatial transcriptomics to systematically characterize TAM heterogeneity and identify prognostic biomarkers in GC. ScRNA-seq analysis revealed nine major cell types (T cells, plasma cells, epithelial cells, fibroblasts, macrophages, endothelial cells, B cells, smooth muscle cells, and mast cells) and distinct macrophage subpopulations with tumor-specific expansion patterns. High-dimensional weighted gene coexpression network analysis identified coexpression modules enriched in GC-associated macrophages. Machine learning algorithms were employed to construct a prognostic signature, and the CoxBoost model demonstrated superior predictive performance across multiple cohorts. The seven-gene signature, including UPP1, VCAN, ELL2, ABCA1, TUBA1A, MX2, and TSPO, showed robust prognostic value in survival prediction. Spatial transcriptomic analysis further revealed distinct metabolic profiles and extensive cellular interaction networks mediated by UPP1-expressing TAMs. These findings provide a comprehensive atlas of TAM heterogeneity and establish novel prognostic biomarkers with potential therapeutic implications in GC.