Prognostic value of M2 macrophage‑related genes and their importance in the immunotherapy response in hepatocellular carcinoma

Liver hepatocellular carcinoma (LIHC) is a major contributor to cancer-associated mortality worldwide, with a poor prognosis due to late-stage diagnosis and limited therapeutic options. M2 macrophages serve crucial roles in the tumor microenvironment (TME), contributing to tumor progression, immune evasion and resistance to therapy. However, the prognostic importance of M2 macrophage-related genes in LIHC and their potential for predicting responses to immunotherapy remain underexplored. A bioinformatics analysis was performed using The Cancer Genome Atlas data to identify M2 macrophage-related genes in LIHC. Differential expression and weighted gene co-expression network analysis were used to identify key gene modules and a prognostic model was developed and validated using Kaplan-Meier analysis and receiver operating characteristic curves. The immune therapy response was assessed using tracking of indels by decomposition and Submap analyses. Killer cell lectin like receptor B1 (KLRB1) was knocked down in HuH-7 cells and overexpressed in HuH-1 cells to evaluate its effect on tumor cells and macrophage regulation. The effect on human umbilical vein endothelial cell tube formation was also assessed. A total of two M2 macrophage-related genes (KLRB1 and phosphatidylinositol-5-phosphate 4-kinase type 2α) were identified as notable prognostic biomarkers for LIHC. The prognostic model demonstrated certain predictive power, with high-risk patients exhibiting markedly worse overall survival. This model was validated in external datasets and associated with immune infiltration patterns. Furthermore, low-risk patients were more likely to respond to immune checkpoint blockade therapy. The inhibition of KLRB1 enhanced LIHC cell activity and increased macrophage polarization from the M0 phenotype to the M2 phenotype by regulating LIHC cell secretions. In conclusion, M2 macrophage-related genes are valuable prognostic biomarkers in LIHC. The prognostic model effectively stratifies patients by survival risk and predicts immunotherapy responses, thereby highlighting the potential for improved TME-targeted therapies in LIHC. The mechanism of KLRB1 regulation in LIHC-macrophage interactions and its impact on LIHC activities were also evaluated.