Han Fang, Cao Dan, Zhu Xin, Shen Lianqiang, Wu Jia, Chen Yizhen, Xu Youyao, Xu Linwei, Cheng Xiangdong, Zhang Yuhua
Hepatobiliary and Pancreatic Surgery Department, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer(IBMC), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
College of Food and Pharmacy, Zhejiang Ocean University, Zhoushan, Zhejiang, China.
Front Oncol. 2023 Jan 5;12:972434. doi: 10.3389/fonc.2022.972434. eCollection 2022.
An increasing number of innovations have been discovered for treating hepatocellular carcinoma (HCC or commonly called HCC) therapy, Ferroptosis and mitochondrial metabolism are essential mechanisms of cell death. These pathways may act as functional molecular biomarkers that could have important clinical significance for determining individual differences and the prognosis of HCC. The aim of this study was to construct a stable and reliable comprehensive model of genetic features and clinical factors associated with HCC prognosis.
In this study, we used RNA-sequencing (fragments per kilobase of exon model per million reads mapped value) data from the Cancer Genome Atlas (TCGA) database to establish a prognostic model. We enrolled 104 patients for further validation. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes enrichment analyses (KEGG) analysis were used for the functional study of differentially expressed genes. Pan-cancer analysis was performed to evaluate the function of the Differentially Expressed Genes (DEGs). Thirteen genes were identified by univariate and least absolute contraction and selection operation (LASSO) Cox regression analysis. The prognostic model was visualized using a nomogram.
We found that eight genes, namely EZH2, GRPEL2, PIGU, PPM1G, SF3B4, TUBG1, TXNRD1 and NDRG1, were hub genes for HCC and differentially expressed in most types of cancer. EZH2, GRPEL2 and NDRG1 may indicate a poor prognosis of HCC as verified by tissue samples. Furthermore, a gene set variation analysis algorithm was created to analyze the relationship between these eight genes and oxidative phosphorylation, mitophagy, and FeS-containing proteins, and it showed that ferroptosis might affect inflammatory-related pathways in HCC.
EZH2, GRPEL2, NDRG1, and the clinical factor of tumor size, were included in a nomogram for visualizing a prognostic model of HCC. This nomogram based on a functional study and verification by clinical samples, shows a reliable performance of patients with HCC.
治疗肝细胞癌（HCC，通常称为肝癌）的创新方法日益增多，铁死亡和线粒体代谢是细胞死亡的重要机制。这些途径可能作为功能性分子生物标志物，对确定肝癌的个体差异和预后具有重要的临床意义。本研究的目的是构建一个稳定可靠的与肝癌预后相关的遗传特征和临床因素综合模型。
在本研究中，我们使用来自癌症基因组图谱（TCGA）数据库的RNA测序（每百万映射 reads 中外显子模型每千碱基片段数）数据建立预后模型。我们招募了104名患者进行进一步验证。基因本体论（GO）和京都基因与基因组百科全书富集分析（KEGG）用于差异表达基因的功能研究。进行泛癌分析以评估差异表达基因（DEG）的功能。通过单变量和最小绝对收缩与选择算子（LASSO）Cox回归分析确定了13个基因。使用列线图对预后模型进行可视化。
我们发现EZH2、GRPEL2、PIGU、PPM1G、SF3B4、TUBG1、TXNRD1和NDRG1这八个基因是肝癌的枢纽基因，并且在大多数类型的癌症中差异表达。组织样本验证表明，EZH2、GRPEL2和NDRG1可能预示肝癌预后不良。此外，创建了一种基因集变异分析算法来分析这八个基因与氧化磷酸化、线粒体自噬和含铁硫蛋白之间的关系，结果表明铁死亡可能影响肝癌中与炎症相关的途径。
EZH2、GRPEL2、NDRG1以及肿瘤大小这一临床因素被纳入列线图，用于可视化肝癌的预后模型。这个基于功能研究并经临床样本验证的列线图，显示出对肝癌患者具有可靠的预测性能。
