Identification of Prognosis-Related Metabolism Genes in Hepatocellular Carcinoma: Constructing a Multi-Gene Model for Risk Stratification - 19/03/26
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Highlight |
• | Dual Mechanism of HCC Progression: This study uncovers the "dual mechanism" of metabolic reprogramming and immune evasion in hepatocellular carcinoma (HCC), emphasizing the role of metabolic genes in immune suppression and tumor progression. |
• | Multi-Omics Approach: By integrating multi-omics data (including single-cell transcriptome analysis and machine learning models), we identified key metabolism-related genes that influence immune microenvironment dynamics in HCC. |
• | Immunotherapy Insights: The findings suggest that metabolic reprogramming in HCC plays a pivotal role in immune evasion, revealing potential targets for immunotherapy and metabolic interventions. |
• | Risk Model Development: A novel metabolism-related gene risk model (MRGRM) is proposed, demonstrating robust prognostic prediction capabilities for HCC patients, with potential applications in precision medicine. |
• | Potential Therapeutic Targets: Our study provides new insights into the role of immune-suppressive cells, such as M2 macrophages and exhausted T cells, in promoting immune evasion in HCC, suggesting novel therapeutic targets to overcome treatment resistance. |
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.
Le texte complet de cet article est disponible en PDF.Keywords : Hepatocellular carcinoma, Metabolic reprogramming, Machine learning, Prognostic analysis model;Metabolism-related genes
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