Macrophage-related Genomic Signatures Predict HCC Prognosis and Therapy Response

<h4>Background/aim</h4>Hepatocellular carcinoma (HCC) is a highly heterogeneous malignancy with poor prognosis due to drug resistance and recurrence. Tumor-associated macrophages (TAMs) are pivotal in the HCC tumor microenvironment, but their prognostic and therapeutic relevance remains incompletely defined. This study aimed to identify macrophage-related genomic signatures, delineate HCC molecular subtypes, and construct a prognostic model to predict survival and therapy response.<h4>Materials and methods</h4>We integrated scRNA-seq (GSE151530) and bulk RNA-seq (HCCDB18, TCGA-HCC) data. Macrophage-related genes were identified via differential expression analysis of scRNA-seq data. Consensus clustering (Euclidean distance, hierarchical clustering) was used for subtype delineation. A prognostic model was constructed using PCA (Principal Component Analysis) on 25 OS-related differentially expressed genes (DEGs; univariate Cox regression), with z-scored normalization and 3 principal components. Immune infiltration (ssGSEA) and drug sensitivity [immunophenoscore (IPS) scores, pRRophetic] were analyzed.<h4>Results</h4>Four HCC subtypes were identified; Cluster C showed the most favorable survival. The PCA-derived score strongly correlated with OS (<i>p</i><0.001) and immunotherapy responsiveness (higher scores=enhanced sensitivity). High scores were associated with increased effector T cell infiltration and reduced T cell exhaustion. Drug sensitivity analyses revealed divergent responses to immunotherapy and conventional agents across subgroups.<h4>Conclusion</h4>Macrophage-related genomic signatures are critical for HCC prognosis and therapy response. The PCA-based model holds promise as a biomarker for personalized therapy, warranting larger cohort validation and mechanistic exploration.