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BACKGROUND: Hepatocellular carcinoma (HCC) is a leading cause of cancer death worldwide, with heterogeneity linked to poor prognosis. Heme metabolism contributes to tumor metabolic reprogramming, but its role in HCC molecular subtyping and prognosis remains underexplored. METHODS: Using TCGA-LIHC and GEO (GSE76427) cohorts, differentially expressed heme metabolism-related genes were identified through differential expression analysis and WGCNA. Heme metabolism-associated molecular subtypes were identified by consensus clustering. A subtype-derived eight‑gene prognostic signature was constructed using LASSO‑Cox regression and validated in an independent dataset. Associations between the signature and immune infiltration, immunotherapy response, TMB, and drug sensitivity were assessed using multiple algorithms. The expression patterns of the signature genes were assessed using Human Protein Atlas (HPA) data, immunohistochemical staining of HCC tissues, and RT-qPCR analysis in HCC cell lines. RESULTS: Four prognostic DEHMRGs classified HCC into two molecular subtypes, with subtype 1 showing worse prognosis. The eight‑gene risk model derived from heme metabolism-associated molecular subtypes performed well in training (1‑year AUC = 0.797) and validation (5‑year AUC = 0.869). High‑risk group exhibited immunosuppressive tumor microenvironment, higher TMB, elevated TIDE scores, and immunotherapy resistance. Low‑risk group was more sensitive to sorafenib, dabrafenib, and erlotinib. HPA analysis provided partial protein-level support, while IHC and RT-qPCR analyses confirmed the differential expression patterns of the eight signature genes in HCC tissues and cell lines. CONCLUSIONS: We constructed a robust prognostic model derived from heme metabolism-associated molecular subtypes for HCC that effectively predicts survival, immune microenvironment, and treatment response, offering insights for personalized therapy and combination strategies targeting heme metabolism.
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Xu et al. (2026) studied this question.
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