Multi-omics and machine learning reveal a senescence-associated lipid-metabolic macrophage phenotype driving immunosuppression and poor prognosis in colorectal cancer

Tumor-associated macrophages (TAMs) are key regulators of the tumor microenvironment in colorectal cancer (CRC), yet their functional heterogeneity remains poorly understood, especially regarding the interplay between cellular senescence and lipid metabolism. We integrated multi-omics and single-cell transcriptomic data to characterize a novel macrophage subpopulation termed senescence-associated lipid-metabolic (SALM) macrophages. Using eight machine learning algorithms, a prognostic model based on SALM signatures was developed and internally evaluated via nested cross-validation in a combined TCGA–GSE39582 cohort. Spatial multiplex immunofluorescence and qPCR were employed for experimental validation. SALM macrophages exhibit elevated IL-10/STAT3 signaling, terminal differentiation, and a core gene module enriched in senescence and lipid metabolism pathways. They interact strongly with T cells via immunosuppressive ligands IL-10 and MIF. Spatial analysis confirmed p16 + CD68 + IL-10 + cell enrichment at the tumor front. The SALM-based prognostic model achieved high accuracy (AUC = 0.972), and a corresponding nomogram showed potential for personalized prognosis prediction. SALM macrophages drive immunosuppression in CRC through a senescence-lipid metabolism axis, serving as a novel prognostic biomarker and potential therapeutic target.