肝细胞癌（HCC）是一种恶性致命肿瘤，癌症干细胞（CSCs）和代谢重编程已被证明在HCC中扮演着不可或缺的角色。本研究旨在揭示代谢重编程与HCC干性特征之间的联系，建立与干性和代谢相关的新基因签名，并将其用于评估HCC的预后和免疫治疗反应。478例HCC患者的临床信息和基因表达谱（GEPs）来源于基因表达杂志库（GEO）和癌症基因组图谱（TCGA）。采用单类逻辑回归（OCLR）算法计算基于信使核糖核酸表达的干性指数（mRNAsi），一种量化干性特征的新型干性指数。对高mRNAsi组和低mRNAsi组进行差异表达分析，并利用来自分子标志数据库（MSigDB）的代谢相关基因组进行差异表达代谢相关基因（DEMRGs）的鉴定。经过综合分析，构建了一个基于三个最有效的预后DEMRGs构成的风险评分模型，包括重组磷酸二酮糖激酶血小板（PFKP），磷酸二酯酶2A（PDE2A）和UDP-葡萄糖醛酸转移酶1A5（UGT1A5）。将HCC患者分为高风险组和低风险组。在两组之间发现了显著差异的通路富集，免疫细胞浸润模式和基因改变。高风险组患者倾向于有更差的临床结果，并更有可能对免疫治疗有反应。生成一个与干性-代谢相关的模型，包括性别，年龄，风险评分模型和肿瘤-淋巴-转移（TNM）分期，并表现出很好的区分能力和强大的预测HCC的预后和免疫治疗反应的能力。Copyright © 2023 Wang, Wan and Du.

Hepatocellular carcinoma (HCC) is a malignant lethal tumor and both cancer stem cells (CSCs) and metabolism reprogramming have been proven to play indispensable roles in HCC. This study aimed to reveal the connection between metabolism reprogramming and the stemness characteristics of HCC, established a new gene signature related to stemness and metabolism and utilized it to assess HCC prognosis and immunotherapy response. The clinical information and gene expression profiles (GEPs) of 478 HCC patients came from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA). The one-class logistic regression (OCLR) algorithm was employed to calculate the messenger ribonucleic acid expression-based stemness index (mRNAsi), a new stemness index quantifying stemness features. Differentially expressed analyses were done between high- and low-mRNAsi groups and 74 differentially expressed metabolism-related genes (DEMRGs) were identified with the help of metabolism-related gene sets from Molecular Signatures Database (MSigDB). After integrated analysis, a risk score model based on the three most efficient prognostic DEMRGs, including Recombinant Phosphofructokinase Platelet (PFKP), phosphodiesterase 2A (PDE2A) and UDP-glucuronosyltransferase 1A5 (UGT1A5) was constructed and HCC patients were divided into high-risk and low-risk groups. Significant differences were found in pathway enrichment, immune cell infiltration patterns, and gene alterations between the two groups. High-risk group patients tended to have worse clinical outcomes and were more likely to respond to immunotherapy. A stemness-metabolism-related model composed of gender, age, the risk score model and tumor-node-metastasis (TNM) staging was generated and showed great discrimination and strong ability in predicting HCC prognosis and immunotherapy response.Copyright © 2023 Wang, Wan and Du.