Wang Shen-Yung, Huang Yen-Hua, Liang Yuh-Jin, Wu Jaw-Ching
Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC.
Division of Gastroenterology and Hepatology, Department of Medicine, MacKay Memorial Hospital, Taipei, Taiwan, ROC.
J Chin Med Assoc. 2022 Oct 1;85(10):972-980. doi: 10.1097/JCMA.0000000000000772. Epub 2022 Jul 8.
Hepatocellular carcinoma (HCC) is among the leading causes of cancer-related death worldwide. The molecular pathogenesis of HCC involves multiple signaling pathways. This study utilizes systems and bioinformatic approaches to investigate the pathogenesis of HCC.
Gene expression microarray data were obtained from 50 patients with chronic hepatitis B and HCC. There were 1649 differentially expressed genes inferred from tumorous and nontumorous datasets. Weighted gene coexpression network analysis (WGCNA) was performed to construct clustered coexpressed gene modules. Statistical analysis was used to study the correlation between gene coexpression networks and demographic features of patients. Functional annotation and pathway inference were explored for each coexpression network. Network analysis identified hub genes of the prognostic gene coexpression network. The hub genes were further validated with a public database.
Five distinct gene coexpression networks were identified by WGCNA. A distinct coexpressed gene network was significantly correlated with HCC prognosis. Pathway analysis of this network revealed extensive integration with cell cycle regulation. Ten hub genes of this gene network were inferred from protein-protein interaction network analysis and further validated in an external validation dataset. Survival analysis showed that lower expression of the 10-gene signature had better overall survival and recurrence-free survival.
This study identified a crucial gene coexpression network associated with the prognosis of hepatitis B virus-related HCC. The identified hub genes may provide insights for HCC pathogenesis and may be potential prognostic markers or therapeutic targets.
肝细胞癌（HCC）是全球癌症相关死亡的主要原因之一。HCC的分子发病机制涉及多个信号通路。本研究利用系统和生物信息学方法来研究HCC的发病机制。
从50例慢性乙型肝炎和HCC患者中获取基因表达微阵列数据。从肿瘤和非肿瘤数据集中推断出1649个差异表达基因。进行加权基因共表达网络分析（WGCNA）以构建聚类共表达基因模块。使用统计分析来研究基因共表达网络与患者人口统计学特征之间的相关性。对每个共表达网络进行功能注释和通路推断。网络分析确定了预后基因共表达网络的枢纽基因。这些枢纽基因在一个公共数据库中得到进一步验证。
通过WGCNA鉴定出五个不同的基因共表达网络。一个独特的共表达基因网络与HCC预后显著相关。对该网络的通路分析显示与细胞周期调控广泛整合。从蛋白质-蛋白质相互作用网络分析中推断出该基因网络的十个枢纽基因，并在外部验证数据集中进一步验证。生存分析表明，10基因特征的低表达具有更好的总生存期和无复发生存期。
本研究确定了一个与乙型肝炎病毒相关HCC预后相关的关键基因共表达网络。所鉴定的枢纽基因可能为HCC发病机制提供见解，并且可能是潜在的预后标志物或治疗靶点。
