School of Science, Jiangnan University, Wuxi, 214122, China.
Wuxi Engineering Research Center for Biocomputing, Jiangnan University, Wuxi, 214122, China.
Interdiscip Sci. 2020 Jun;12(2):226-236. doi: 10.1007/s12539-020-00366-8. Epub 2020 Apr 15.
Hepatocellular carcinoma (HCC) is a common cancer of high mortality, mainly due to the difficulty in diagnosis during its clinical stage. Here we aim to find the gene biomarkers, which are of important significance for diagnosis and treatment. In this work, 3682 differentially expressed genes on HCC were firstly differentiated based on the Cancer Genome Atlas database (TCGA). Co-expression modules of these differentially expressed genes were then constructed based on the weighted correlation network algorithm. The correlation coefficient between the co-expression module and clinical data from the Broad GDAC Firehose was thereafter derived. Finally, the interactive network of genes was then constructed. Then, the hub genes were used to implement enrichment analysis and pathway analysis in the Database for Annotation, Visualization and Integrated Discovery (DAVID) database. Results revealed that the abnormally expressed genes in the module played an important role in the biological process including cell division, sister chromatid cohesion, DNA repair, and G1/S transition of mitotic cell cycle. Meanwhile, these genes also enriched in a few crucial pathways related to Cell cycle, Oocyte meiosis, and p53 signaling. Via investigating the closeness centrality of the interactive network, eight gene biomarkers including the CKAP2, TPX2, CDCA8, KIFC1, MELK, SGO1, RACGAP1, and KIAA1524 gene were discovered, whose functions had been indeed revealed to be correlated with HCC. This study, therefore, suggests that the abnormal expression of those eight genes may be taken as gene biomarkers of HCC.
肝细胞癌（HCC）是一种高死亡率的常见癌症，主要是由于其在临床阶段的诊断困难。在这里，我们旨在寻找基因生物标志物，这对诊断和治疗具有重要意义。在这项工作中，我们首先基于癌症基因组图谱数据库（TCGA）区分了 HCC 上的 3682 个差异表达基因。然后基于加权相关网络算法构建这些差异表达基因的共表达模块。之后，得出共表达模块与 Broad GDAC Firehose 临床数据之间的相关系数。最后，构建基因的交互网络。然后，使用 DAVID 数据库中的富集分析和途径分析来实现枢纽基因。结果表明，模块中异常表达的基因在细胞分裂、姐妹染色单体黏合、DNA 修复和有丝分裂细胞周期的 G1/S 过渡等生物学过程中发挥重要作用。同时，这些基因也富集在与细胞周期、卵母细胞减数分裂和 p53 信号相关的几个关键途径中。通过研究交互网络的接近中心性，发现了八个基因生物标志物，包括 CKAP2、TPX2、CDCA8、KIFC1、MELK、SGO1、RACGAP1 和 KIAA1524 基因，其功能确实与 HCC 相关。因此，本研究表明，这些八个基因的异常表达可能被视为 HCC 的基因生物标志物。
