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.