Table_4_Identification of Energy Metabolism Genes for the Prediction of Survival in Hepatocellular Carcinoma.XLSX

Hepatocellular carcinoma (HCC) samples were clustered into three energy metabolism-related molecular subtypes (C1, C2, and C3) with different prognosis using the gene expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). HCC energy metabolism-related molecular subtype analysis was conducted based on the 594 energy metabolism genes. Differential expression analysis yielded 576 differentially expressed genes (DEGs) among the three subtypes, which were closely related to HCC progression. Six genes were finally selected from the 576 DEGs through LASSO-Cox regression and used in constructing a six-gene signature-associated prognostic risk model, which was validated using the TCGA internal and three GEO external validation cohorts. The risk model showed that high ANLN, ENTPD2, TRIP13, PLAC8, and G6PD expression levels were associated with bad prognosis, and high expression of ADH1C was associated with a good prognosis. The validation results showed that our risk model had a high distinguishing ability of prognosis in HCC patients. The four enriched pathways of the risk model were obtained by gene set enrichment analysis (GSEA) and found to be associated with the tumorigenesis and development of HCC, including the cell cycle, Wnt signaling pathway, drug metabolism cytochrome P450, and primary bile acid biosynthesis. The risk score calculated from the established risk model in 204 samples and other clinical characteristics were used in building a nomogram with a good prognostic prediction ability (C-index = 0.746, 95% CI = 0.714–0.777). The area under the curves (AUCs) of the nomogram model in 1-, 2-, and 3-years were 0.82, 0.77, and 0.79, respectively. Then, qRT-PCR and immunohistochemistry were used to validate the mRNA expression levels of the six genes, and significant differences in mRNA and gene expression were observed among the tumor and adjacent tissues. Overall, our study divided HCC patients into three energy metabolism-related molecular subtypes with different prognosis. Then, a risk model with a good performance in prognostic prediction was built using the TCGA dataset. This model can be used as an independent prognostic evaluation index for HCC patients.

本研究利用癌症基因组图谱（The Cancer Genome Atlas, TCGA）及基因表达综合数据库（Gene Expression Omnibus, GEO）的基因表达数据，将肝细胞癌（Hepatocellular carcinoma, HCC）样本聚类为三种与能量代谢相关的分子亚型（C1、C2、C3），各亚型预后存在显著差异。本研究基于594个能量代谢基因开展HCC能量代谢相关分子亚型分析。差异表达分析在三种亚型间筛选得到576个差异表达基因（differentially expressed genes, DEGs），这些基因与HCC进展密切相关。研究人员通过LASSO-Cox回归分析从576个DEGs中最终筛选出6个基因，用于构建基于六基因特征的预后风险模型，并利用TCGA内部队列及3个GEO外部队列对该模型进行验证。该风险模型显示，ANLN、ENTPD2、TRIP13、PLAC8及G6PD的高表达与不良预后相关，而ADH1C的高表达则与良好预后相关。验证结果表明，本研究构建的风险模型对HCC患者的预后具有较高的区分能力。通过基因集富集分析（gene set enrichment analysis, GSEA），本研究得到该风险模型的4条富集通路，这些通路与HCC的发生发展密切相关，包括细胞周期、Wnt信号通路、药物代谢细胞色素P450以及初级胆汁酸生物合成。研究人员利用204例样本中基于已构建风险模型计算得到的风险评分及其他临床特征，构建了具有良好预后预测能力的列线图（nomogram），其C指数为0.746，95%置信区间为0.714~0.777。该列线图模型在1年、2年及3年的受试者工作特征曲线下面积（area under the curves, AUCs）分别为0.82、0.77及0.79。随后，采用定量实时聚合酶链反应（qRT-PCR）及免疫组化技术对6个基因的mRNA表达水平进行验证，结果发现肿瘤组织与癌旁组织间的mRNA及基因表达存在显著差异。综上，本研究将HCC患者分为三种与能量代谢相关、预后存在差异的分子亚型；基于TCGA数据集构建的预后风险模型表现优异，可作为HCC患者独立的预后评估指标。