Li Boxuan, Pu Ke, Wu Xinan
The First School of Clinical Medicine, Lanzhou University, Lanzhou, China.
Department of Pharmacy, The First Hospital of Lanzhou University, Lanzhou, China.
J Cell Biochem. 2019 Jul;120(7):11418-11431. doi: 10.1002/jcb.28420. Epub 2019 Feb 11.
Hepatocellular carcinoma (HCC) is a highly malignant tumor found in the bile duct epithelial cells, and the second most common tumor of the liver. However, the pivotal roles of most molecules of tumorigenesis in HCC are still unclear. Hence, it is essential to detect the tumorigenic mechanism and develop novel prognostic biomarkers for clinical application. The data of HCC mRNA-seq and clinical information from The Cancer Genome Atlas (TCGA) database were analyzed by weighted gene co-expression network analysis (WGCNA). Co-expression modules and clinical traits were constructed by the Pearson correlation analysis, interesting modules were selected and gene ontology and pathway enrichment analysis were performed. Intramodule analysis and protein-protein interaction construction of selected modules were conducted to screen hub genes. In addition, upstream transcription factors and microRNAs of hub genes were predicted by miRecords and NetworkAnalyst database. Afterward, a high connectivity degree of hub genes from two networks was picked out to perform the differential expression validation in the Gene Expression Profiling Interactive Analysis database and Human Protein Atlas database and survival analysis in Kaplan-Meier plotter online tool. By utilizing WGCNA, several hub genes that regulate the mechanism of tumorigenesis in HCC were identified, which was associated with clinical traits including the pathological stage, histological grade, and liver function. Surprisingly, ZWINT, CENPA, RACGAP1, PLK1, NCAPG, OIP5, CDCA8, PRC1, and CDK1 were identified statistically as hub genes in the blue module, which were closely implicated in pathological T stage and histologic grade of HCC. Moreover, these genes also were strongly associated with the HCC cell growth and division. Network and survival analyses found that nine hub genes may be considered theoretically as indicators to predict the prognosis of patients with HCC or clinical treatment target, it will be necessary for basic experiments and large-scale cohort studies to validate further.
肝细胞癌（HCC）是一种在胆管上皮细胞中发现的高度恶性肿瘤，是肝脏第二常见的肿瘤。然而，大多数分子在HCC肿瘤发生中的关键作用仍不清楚。因此，检测肿瘤发生机制并开发新的预后生物标志物以供临床应用至关重要。通过加权基因共表达网络分析（WGCNA）对来自癌症基因组图谱（TCGA）数据库的HCC mRNA测序数据和临床信息进行分析。通过Pearson相关分析构建共表达模块和临床特征，选择感兴趣的模块并进行基因本体和通路富集分析。对所选模块进行模块内分析和蛋白质-蛋白质相互作用构建以筛选枢纽基因。此外，通过miRecords和NetworkAnalyst数据库预测枢纽基因的上游转录因子和微小RNA。之后，从两个网络中挑选出具有高连接度的枢纽基因，在基因表达谱交互式分析数据库和人类蛋白质图谱数据库中进行差异表达验证，并在Kaplan-Meier绘图仪在线工具中进行生存分析。通过利用WGCNA，鉴定出了几个调节HCC肿瘤发生机制的枢纽基因，这些基因与包括病理分期、组织学分级和肝功能在内的临床特征相关。令人惊讶的是，ZWINT、CENPA、RACGAP1、PLK1、NCAPG、OIP5、CDCA8、PRC1和CDK1在蓝色模块中经统计学鉴定为枢纽基因，它们与HCC的病理T分期和组织学分级密切相关。此外，这些基因也与HCC细胞的生长和分裂密切相关。网络和生存分析发现，理论上九个枢纽基因可被视为预测HCC患者预后或临床治疗靶点的指标，有必要通过基础实验和大规模队列研究进一步验证。
