Guan Lianyue, Luo Qiang, Liang Na, Liu Hongyu
Department of Hepatobiliary-Pancreatic Surgery, China-Japan Union Hospital of Jilin University, Changchun, Jilin 130033, P.R. China.
Department of Ultrasound, China-Japan Union Hospital of Jilin University, Changchun, Jilin 130033, P.R. China.
Exp Ther Med. 2019 Jun;17(6):4506-4516. doi: 10.3892/etm.2019.7494. Epub 2019 Apr 17.
In the present study, gene expression data of hepatocellular carcinoma (HCC) were analyzed by using a multi-step Bioinformatics approach to establish a novel prognostic prediction system. Gene expression profiles were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The overlapping differentially expressed genes (DEGs) between these two datasets were identified using the package in R. Prognostic genes were further identified by Cox regression using the package. The significantly co-expressed gene pairs were selected using the R function to construct the co-expression network. Functional and module analyses were also performed. Next, a prognostic prediction system was established by Bayes discriminant analysis using the function in the package, which was further validated in another independent GEO dataset. A total of 177 overlapping DEGs were identified from TCGA and the GEO dataset (GSE36376). Furthermore, 161 prognostic genes were selected and the top six were stanniocalcin 2, carbonic anhydrase 12, cell division cycle (CDC) 20, deoxyribonuclease 1 like 3, glucosylceramidase β3 and metallothionein 1G. A gene co-expression network involving 41 upregulated and 52 downregulated genes was constructed. SPC24, endothelial cell specific molecule 1, CDC20, CDCA3, cyclin (CCN) E1 and chromatin licensing and DNA replication factor 1 were significantly associated with cell division, mitotic cell cycle and positive regulation of cell proliferation. CCNB1, CCNE1, CCNB2 and stratifin were clearly associated with the p53 signaling pathway. A prognostic prediction system containing 55 signature genes was established and then validated in the GEO dataset GSE20140. In conclusion, the present study identified a number of prognostic genes and established a prediction system to assess the prognosis of HCC patients.
在本研究中，通过多步骤生物信息学方法分析肝细胞癌（HCC）的基因表达数据，以建立一种新的预后预测系统。基因表达谱从癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）下载。使用R中的包识别这两个数据集之间重叠的差异表达基因（DEG）。使用R中的包通过Cox回归进一步鉴定预后基因。使用R函数选择显著共表达的基因对以构建共表达网络。还进行了功能和模块分析。接下来，使用R中的包中的函数通过贝叶斯判别分析建立预后预测系统，并在另一个独立的GEO数据集中进一步验证。从TCGA和GEO数据集（GSE36376）中总共鉴定出177个重叠的DEG。此外，选择了161个预后基因，前六个是2型骨钙素、碳酸酐酶12、细胞分裂周期（CDC）20、脱氧核糖核酸酶1样3、β3葡糖神经酰胺酶和金属硫蛋白1G。构建了一个包含41个上调基因和52个下调基因的基因共表达网络。SPC24、内皮细胞特异性分子1、CDC20、CDCA3、细胞周期蛋白（CCN）E1和染色质许可与DNA复制因子1与细胞分裂、有丝分裂细胞周期和细胞增殖的正调控显著相关。CCNB1、CCNE1、CCNB2和stratifin与p53信号通路明显相关。建立了一个包含55个特征基因的预后预测系统，然后在GEO数据集GSE20140中进行验证。总之，本研究鉴定了一些预后基因，并建立了一个预测系统来评估HCC患者的预后。
