He Jin, Li Binbin, Liu Huize, Chu Weijian, Rao Chunhui
Department of Colorectal Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China.
Transl Cancer Res. 2025 May 30;14(5):2940-2955. doi: 10.21037/tcr-2024-2621. Epub 2025 May 27.
Pyruvate metabolism presents a novel, therapeutically targetable metabolic vulnerability in hepatocellular carcinoma (HCC). In this study, we sought to identify HCC molecular subtypes and develop prognostic signatures based on pyruvate metabolism-related genes (PMRGs) to inform personalized therapeutic approaches.
Transcriptional profiles and clinical data of HCC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Consensus clustering was employed for molecular classification, while a least absolute shrinkage and selection operator (LASSO) Cox regression model was constructed for risk score calculation. The relationship between the risk score and HCC prognosis, immune landscape, gene expression, and drug sensitivity was analyzed.
Twenty PMRGs were identified as significantly associated with HCC prognosis. Consensus clustering of these genes revealed two distinct molecular subtypes that stratified patients into groups with favorable and unfavorable outcomes. A novel six-gene signature, comprising , , , , , and , was developed for HCC prognostication. The receiver operating characteristic (ROC) curve demonstrated robust survival prediction in all cohorts, allowing the stratification of patients into high- and low-risk groups with markedly different overall survival (OS). The signature-derived nomogram displayed appreciable clinical net benefit. Enrichment analysis revealed activation of PMRGs and enrichment of diverse metabolic processes and signaling pathways in the high-risk group. Moreover, the prognostic signature showed significant correlations with immune landscapes and therapeutic responses, enabling prediction of immunotherapy responsiveness.
Collectively, a unique PMRG-based signature effectively predicts prognosis in HCC patients and provides valuable insights into chemotherapy and immunotherapy strategies for these individuals.
丙酮酸代谢是肝细胞癌(HCC)中一种新的、具有治疗靶点的代谢脆弱性。在本研究中,我们试图识别HCC分子亚型,并基于丙酮酸代谢相关基因(PMRGs)开发预后特征,以指导个性化治疗方法。
从癌症基因组图谱(TCGA)和基因表达综合数据库(GEO)数据集中获取HCC患者的转录谱和临床数据。采用一致性聚类进行分子分类,同时构建最小绝对收缩和选择算子(LASSO)Cox回归模型计算风险评分。分析风险评分与HCC预后、免疫图谱、基因表达和药物敏感性之间的关系。
20个PMRGs被确定与HCC预后显著相关。对这些基因进行一致性聚类,发现了两种不同的分子亚型,可将患者分为预后良好和不良的组。开发了一种新的由六个基因组成的特征,包括[此处原文缺失六个基因具体名称],用于HCC预后评估。受试者工作特征(ROC)曲线显示在所有队列中均有强大的生存预测能力,可将患者分为总生存期(OS)明显不同的高风险组和低风险组。基于特征的列线图显示出可观的临床净效益。富集分析显示高风险组中PMRGs的激活以及多种代谢过程和信号通路的富集。此外,预后特征与免疫图谱和治疗反应显著相关,能够预测免疫治疗反应性。
总的来说,一种独特的基于PMRGs的特征能够有效地预测HCC患者的预后,并为这些患者的化疗和免疫治疗策略提供有价值的见解。