Discover oncology · 2025
A novel immune-metabolic prognostic model stratifies survival and personalizes therapy in hepatocellular carcinoma.
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Lin Zejin, Wang Jiaheng, Li Yiping, Yi Taijun, Huang Xiaoyue, Zhong Chengrui, Chen Zhiping, Lin Zeyu
Аннотация
BACKGROUND: The etiology of HCC is multifactorial, with pathogenesis involving immune and metabolic pathways. This study developed a prognostic model based on immune- and metabolism-related genes (IMRGs) to improve personalized HCC management.
METHODS: Transcriptomic data from HCC patients were obtained from TCGA (n = 377) and GEO (n = 115). Differentially expressed IMRGs between tumor and adjacent normal tissues were identified, and patients were stratified using non-negative matrix factorization (NMF). A LASSO-Cox prognostic model was constructed and validated, with assessments of immune microenvironment, therapeutic sensitivity, and functional pathways.
RESULTS: HCC patients were classified into two subgroups with distinct immune microenvironment features. LASSO regression identified nine key prognostic genes from 54 consensus IMRGs, forming a robust risk model. High-risk patients had significantly worse survival, and the model outperformed existing immune/metabolic signatures (5-year AUC = 0.700 vs. 0.570-0.603) and clinical parameters (AUC = 0.720 vs. 0.470-0.712). Functional analysis revealed enhanced chromosome separation and nuclear division in high-risk patients, while low-risk patients showed elevated amino acid catabolism and fatty acid metabolism. High-risk patients also exhibited reduced Natural Killer cell activity and type II interferon responses but increased oxaliplatin sensitivity and 5-Fluorouracil resistance.
CONCLUSION: This IMRG-based prognostic model effectively predicts HCC outcomes, enabling risk stratification and personalized therapy. It demonstrates strong clinical utility, advancing precision medicine in HCC management.
METHODS: Transcriptomic data from HCC patients were obtained from TCGA (n = 377) and GEO (n = 115). Differentially expressed IMRGs between tumor and adjacent normal tissues were identified, and patients were stratified using non-negative matrix factorization (NMF). A LASSO-Cox prognostic model was constructed and validated, with assessments of immune microenvironment, therapeutic sensitivity, and functional pathways.
RESULTS: HCC patients were classified into two subgroups with distinct immune microenvironment features. LASSO regression identified nine key prognostic genes from 54 consensus IMRGs, forming a robust risk model. High-risk patients had significantly worse survival, and the model outperformed existing immune/metabolic signatures (5-year AUC = 0.700 vs. 0.570-0.603) and clinical parameters (AUC = 0.720 vs. 0.470-0.712). Functional analysis revealed enhanced chromosome separation and nuclear division in high-risk patients, while low-risk patients showed elevated amino acid catabolism and fatty acid metabolism. High-risk patients also exhibited reduced Natural Killer cell activity and type II interferon responses but increased oxaliplatin sensitivity and 5-Fluorouracil resistance.
CONCLUSION: This IMRG-based prognostic model effectively predicts HCC outcomes, enabling risk stratification and personalized therapy. It demonstrates strong clinical utility, advancing precision medicine in HCC management.