Table_1_Identification of Mitochondrial-Related Prognostic Biomarkers Associated With Primary Bile Acid Biosynthesis and Tumor Microenvironment of Hepatocellular Carcinoma.xlsx

Hepatocellular carcinoma (HCC) is one of the leading causes of tumor-associated deaths worldwide. Despite great progress in early diagnosis and multidisciplinary tumor management, the long-term prognosis of HCC remains poor. Currently, metabolic reprogramming during tumor development is widely observed to support rapid growth and proliferation of cancer cells, and several metabolic targets that could be used as cancer biomarkers have been identified. The liver and mitochondria are the two centers of human metabolism at the whole organism and cellular levels, respectively. Thus, identification of prognostic biomarkers based on mitochondrial-related genes (Mito-RGs)—the coding-genes of proteins located in the mitochondria—that reflect metabolic changes associated with HCC could lead to better interventions for HCC patients. In the present study, we used HCC data from The Cancer Genome Atlas (TCGA) database to construct a classifier containing 10 Mito-RGs (ACOT7, ADPRHL2, ATAD3A, BSG, FAM72A, PDK3, PDSS1, RAD51C, TOMM34, and TRMU) for predicting the prognosis of HCC by using 10-fold Least Absolute Shrinkage and Selection Operation (LASSO) cross-validation Cox regression. Based on the risk score calculated by the classifier, the samples were divided into high- and low-risk groups. Gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), t-distributed stochastic neighbor embedding (t-SNE), and consensus clusterPlus algorithms were used to identify metabolic pathways that were significantly different between the high- and low-risk groups. We further investigated the relationship between metabolic status and infiltration of immune cells into HCC tumor samples by using the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm combined with the Tumor Immune Estimation Resource (TIMER) database. Our results showed that the classifier based on Mito-RGs could act as an independent biomarker for predicting survival of HCC patients. Repression of primary bile acid biosynthesis plays a vital role in the development and poor prognosis of HCC, which provides a potential approach to treatment. Our study revealed cross-talk between bile acid and infiltration of tumors by immune cells, which may provide novel insight into immunotherapy of HCC. Furthermore, our research may provide a novel method for HCC metabolic therapy based on modulation of mitochondrial function.

肝细胞癌（Hepatocellular carcinoma, HCC）是全球范围内与肿瘤相关死亡的主要诱因之一。尽管早期诊断与多学科肿瘤管理已取得显著进展，肝细胞癌的长期预后仍不容乐观。当前，肿瘤发生过程中的代谢重编程已被广泛证实可支持癌细胞快速生长与增殖，且已有多个可作为癌症生物标志物的代谢靶点被成功鉴定。肝脏与线粒体分别是整个人体代谢与细胞代谢的两大核心枢纽。因此，基于线粒体相关基因（mitochondrial-related genes, Mito-RGs）——即编码定位于线粒体的蛋白质的基因——筛选可反映肝细胞癌相关代谢变化的预后生物标志物，有望为肝细胞癌患者提供更优化的干预策略。
本研究利用癌症基因组图谱（The Cancer Genome Atlas, TCGA）数据库中的肝细胞癌数据，通过10折最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operation, LASSO）交叉验证Cox回归模型，构建了包含10个线粒体相关基因（ACOT7、ADPRHL2、ATAD3A、BSG、FAM72A、PDK3、PDSS1、RAD51C、TOMM34及TRMU）的分类器，用于预测肝细胞癌患者的预后。根据该分类器计算得到的风险评分，我们将样本划分为高风险组与低风险组。随后通过基因集富集分析（gene set enrichment analysis, GSEA）、基因集变异分析（gene set variation analysis, GSVA）、t分布邻域嵌入（t-distributed stochastic neighbor embedding, t-SNE）及ConsensusClusterPlus算法，鉴定出高、低风险组间存在显著差异的代谢通路。
本研究进一步结合基于估计RNA转录本相对子集的细胞类型鉴定（Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts, CIBERSORT）算法与肿瘤免疫评估资源（Tumor Immune Estimation Resource, TIMER）数据库，探究了肝细胞癌肿瘤样本的代谢状态与免疫细胞浸润之间的关联。研究结果表明，基于线粒体相关基因构建的分类器可作为独立生物标志物，用于预测肝细胞癌患者的生存情况。原发性胆汁酸生物合成通路的抑制在肝细胞癌的发生发展与不良预后中发挥关键作用，这为肝细胞癌的治疗提供了潜在方向。本研究揭示了胆汁酸与肿瘤免疫细胞浸润之间的交叉调控关系，可为肝细胞癌的免疫治疗提供全新的研究视角。此外，本研究还可为基于线粒体功能调控的肝细胞癌代谢治疗提供一种新颖的方法。