Identification of prognosis-related metabolism genes in hepatocellular carcinoma: constructing a multi-gene model for risk stratification.
[OBJECTIVE] Hepatocellular carcinoma (HCC) is a heterogeneous malignancy with poor prognosis.
APA
Cai Z, Huang C, et al. (2026). Identification of prognosis-related metabolism genes in hepatocellular carcinoma: constructing a multi-gene model for risk stratification.. Current research in translational medicine, 74(2), 103582. https://doi.org/10.1016/j.retram.2026.103582
MLA
Cai Z, et al.. "Identification of prognosis-related metabolism genes in hepatocellular carcinoma: constructing a multi-gene model for risk stratification.." Current research in translational medicine, vol. 74, no. 2, 2026, pp. 103582.
PMID
41895243
Abstract
[OBJECTIVE] Hepatocellular carcinoma (HCC) is a heterogeneous malignancy with poor prognosis. This study identifies metabolism-related genes (MRGs) associated with HCC prognosis, develops a multi-gene prognostic model based on metabolic reprogramming and immune escape, and evaluates their roles in the tumor microenvironment (TME) to guide diagnosis and treatment.
[METHODS] Transcriptomic and clinical data from HCC patients were analyzed using public databases (TCGA). MRGs linked to HCC staging and prognosis were identified. Weighted gene co-expression network analysis (WGCNA) detected metabolic gene modules associated with tumor progression. A multi-gene prognostic model was built using LASSO and random survival forests (RSF). Model performance was evaluated with Kaplan-Meier analysis, ROC curves, and Nomogram. Single-cell analyses explored metabolic interactions, and enrichment and mutation analyses assessed key genes' significance. PCR validated gene expression.
[RESULTS] A total of 374 metabolism-related genes were linked to HCC staging. A prognostic model with eight key genes (UCK2, CAD, NUDT1, PIGU, IVD, CAT, ALDH6A1, SLC2A2) showed strong predictive performance across TCGA, ICGC, and GEO cohorts. Low-risk patients had significantly better survival (5-year survival prediction AUC of 0.75). PCR validation confirmed differential expression: UCK2, CAD, NUDT1, and PIGU were upregulated, while IVD, CAT, ALDH6A1, and SLC2A2 were downregulated. Immune infiltration analysis indicated an accumulation of immunosuppressive cells in the high-risk group, whereas the low-risk group exhibited an immune-active phenotype characterized by elevated infiltration of effector cells. Single-cell analysis uncovered metabolic-immune interactions in the TME. Gene mutation analysis showed frequent mutations in the high-risk group, linked to invasiveness and treatment resistance.
[CONCLUSION] This study identifies key metabolism-related genes linked to HCC prognosis and develops a multi-gene prognostic model. Our findings highlight the roles of metabolic reprogramming and immune escape in HCC, providing a foundation for future immune and metabolic interventions.
[METHODS] Transcriptomic and clinical data from HCC patients were analyzed using public databases (TCGA). MRGs linked to HCC staging and prognosis were identified. Weighted gene co-expression network analysis (WGCNA) detected metabolic gene modules associated with tumor progression. A multi-gene prognostic model was built using LASSO and random survival forests (RSF). Model performance was evaluated with Kaplan-Meier analysis, ROC curves, and Nomogram. Single-cell analyses explored metabolic interactions, and enrichment and mutation analyses assessed key genes' significance. PCR validated gene expression.
[RESULTS] A total of 374 metabolism-related genes were linked to HCC staging. A prognostic model with eight key genes (UCK2, CAD, NUDT1, PIGU, IVD, CAT, ALDH6A1, SLC2A2) showed strong predictive performance across TCGA, ICGC, and GEO cohorts. Low-risk patients had significantly better survival (5-year survival prediction AUC of 0.75). PCR validation confirmed differential expression: UCK2, CAD, NUDT1, and PIGU were upregulated, while IVD, CAT, ALDH6A1, and SLC2A2 were downregulated. Immune infiltration analysis indicated an accumulation of immunosuppressive cells in the high-risk group, whereas the low-risk group exhibited an immune-active phenotype characterized by elevated infiltration of effector cells. Single-cell analysis uncovered metabolic-immune interactions in the TME. Gene mutation analysis showed frequent mutations in the high-risk group, linked to invasiveness and treatment resistance.
[CONCLUSION] This study identifies key metabolism-related genes linked to HCC prognosis and develops a multi-gene prognostic model. Our findings highlight the roles of metabolic reprogramming and immune escape in HCC, providing a foundation for future immune and metabolic interventions.
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