Multi-Omics Deciphering of the Characteristics, Functional Mechanisms, and Prognostic Value of Tumor-Associated Macrophage Subsets in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a highly prevalent malignancy with a poor prognosis and limited response to immunotherapy, largely due to its heterogeneous tumor microenvironment (TME). Tumor-associated macrophages (TAMs) are key regulators in the TME, though their subsets and clinical roles remain incompletely understood. To address this, we integrated multi-omics data and performed single-cell transcriptome clustering, identifying five distinct TAM subsets. Among these, the SPP1+/TREM2 + subset (TAM_0) was highlighted as an independent prognostic risk factor, associated with advanced disease stage, immunosuppressive TME remodeling, and upregulation of immune checkpoint genes. Based on these findings, a robust six-gene prognostic model (including SPP1, SLC11A1, HK2, BCAT1, PHLDA2, and ANP32E) was constructed and validated across multiple cohorts, demonstrating high accuracy in predicting overall survival. Spatial transcriptomics further confirmed that these genes and related metabolic pathways were specifically enriched in tumor regions. This study systematically delineates TAM heterogeneity in HCC, identifies a key immunosuppressive TAM subset, and provides a clinically applicable prognostic model for risk stratification and personalized treatment.