ArticleFrontiers in immunology2026
Macrophage-linked lipid metabolic signatures for HCC prognosis identified through integrated bulk and single-cell transcriptomics.
Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Macrophage-dependent immune remodeling and altered lipid metabolism are closely related to liver cancer behavior. In hepatocellular carcinoma (HCC), the survival relevance of macrophage-associated lipid metabolism genes (MLMGs) remains insufficiently characterized. This work evaluated MLMG-related molecular features connected with HCC progression and prognosis. Methods: Public HCC transcriptomic and single-cell cohorts were analyzed. Macrophage-related differential genes (MRDGs) extracted from GSE149614 were combined with TCGA-HCC differentially expressed genes1 (DEGs1) and lipid metabolism-related genes (LMRGs). Shared candidates were screened through univariate Cox analysis and machine-learning procedures to obtain prognostic MLMGs and build a risk-score model. Immune profiling, enrichment analysis, cell-cell communication inference, pseudo-time reconstruction, and RT-qPCR validation were then performed. Results: FABP5 and ECHS1 constituted the final two-gene prognostic signature. Risk categories were accompanied by altered immune components, including activated B cells, and pathways such as "fatty acid omega hydroxylase activity". The high-risk group suggested potentially stronger immune escape-related features and higher checkpoint expression. Communication analysis suggested stronger interactions between CD8+ T cells and natural killer (NK) cells, whereas pseudotime analysis showed that MLMG expression may be associated with changes in CD8+ T-cell states. RT-qPCR preliminarily validated increased FABP5 expression and decreased ECHS1 expression in patients with HCC. Conclusion: FABP5 and ECHS1 were selected as prognostic MLMGs in HCC. This two-gene model may provide clues for survival assessment and subsequent mechanism-oriented research.
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