Macrophage Polarization in the Tumor Microenvironment of Hepatocellular Carcinoma: From Mechanistic Insights to Translational Therapies

Background : Hepatocellular carcinoma (HCC) harbors a dynamic tumor microenvironment (TME) in which macrophages are highly abundant and plastic. Under physiological conditions, macrophages switch between inflammatory and resolution/tissue-repair programs to maintain homeostasis; however, during hepatocarcinogenesis these programs are reprogrammed into tumor-associated macrophage (TAM) states that foster immune suppression, angiogenesis, and tumor progression. Purpose : To summarize macrophage heterogeneity and polarization mechanisms in HCC, and to highlight omics-informed therapeutic opportunities for targeting TAMs and improving precision immunotherapy. Research Design : This review summarizes physiological macrophage polarization and the mechanistic basis of macrophage reprogramming in the hepatocellular carcinoma immune microenvironment, integrating evidence from recent advances in single-cell sequencing, multi-omics, and spatial transcriptomics, with a focus on macrophage subset diversity, key regulatory pathways governing polarization and function, and emerging macrophage-targeted interventions and biomarkers. Results: Recent single-cell and spatial multi-omics studies reveal substantial TAM heterogeneity and plasticity in HCC. Macrophage-targeted strategies—including TAM depletion, phenotypic reprogramming, and exosome-mediated drug delivery—show encouraging preclinical efficacy. Macrophage-associated prognostic models and biomarkers may support individualized immunotherapeutic approaches. Conclusions : Macrophage polarization in HCC represents a dynamic continuum that is essential for homeostasis but is co-opted by tumors to drive immunosuppression and tissue remodeling. Advances in single-cell and spatial multi-omics are redefining TAM subsets and actionable pathways, enabling more rational macrophage-targeted therapies. However, challenges remain in standardizing TAM definitions, identifying robust predictive biomarkers, minimizing off-target effects, and optimizing combinations with immunotherapy. Integrating longitudinal multi-omics with AI-based modeling may help predict macrophage state transitions, guide patient-specific regimens, and advance precision medicine in HCC.