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Tumor\u2010associated macrophages (TAMs) represent the most abundant immune cell population in the TME and exhibit remarkable functional plasticity. This study integrated single\u2010cell RNA\u2010sequencing (scRNA\u2010seq) data, bulk transcriptomics, and spatial transcriptomics to systematically characterize TAM heterogeneity and identify prognostic biomarkers in GC. ScRNA\u2010seq 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\u2010specific expansion patterns. High\u2010dimensional weighted gene coexpression network analysis identified coexpression modules enriched in GC\u2010associated macrophages. Machine learning algorithms were employed to construct a prognostic signature, and the CoxBoost model demonstrated superior predictive performance across multiple cohorts. The seven\u2010gene 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\u2010expressing TAMs. These findings provide a comprehensive atlas of TAM heterogeneity and establish novel prognostic biomarkers with potential therapeutic implications in GC.<\/jats:p>","DOI":"10.1155\/humu\/6130883","type":"journal-article","created":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T11:04:03Z","timestamp":1779879843000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrative Multiomics Analysis Reveals Tumor\u2010Associated Macrophage Heterogeneity and a Prognostic Signature in Gastric 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