Yang M-R, Zhang Y, Wu X-X, Chen W
Department of Hepatopathy,Wuxi No. 5 People's Hospital, Wuxi, China.
Eur Rev Med Pharmacol Sci. 2016 Oct;20(20):4248-4256.
RNA-seq data of hepatocellular carcinoma (HCC) was analyzed to identify critical genes related to the pathogenesis and prognosis.
Three RNA-seq datasets of HCC (GSE69164, GSE63863 and GSE55758) were downloaded from Gene Expression Omnibus (GEO), while another dataset including 54 HCC cases with survival time was obtained from The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) were identified by significant analysis of microarrays (SAM) method using package samr of R. As followed, we constructed a protein-protein interaction (PPI) network based on the information in Human Protein Reference Database (HPRD). Modules in the PPI network were identified with MCODE method using plugin clusterViz of CytoScape. Gene Ontology (GO) enrichment analysis and pathway enrichment analysis were performed with DAVID. The difference in survival curves was analyzed with Kaplan-Meier (K-M) method using package survival.
A total of 2572 DEGs were identified in the 3 datasets from GEO (GSE69164, GSE63863 and GSE55758). The PPI network was constructed including 660 nodes and 1008 edges, and 4 modules were disclosed in the network. Module A (containing 244 DEGs) was found to related to HCC closely, which genes were involved in transcription factor binding, protein metabolism as well as regulation of apoptosis. Nine hub genes were identified in the module A, including PRKCA, YWHAZ, KRT18, NDRG1, HSPA1A, HSP90AA1, HSF1, IKGKB and UBE21. The network provides the protein-protein interaction of these critical genes, which were implicated in the pathogenesis of HCC. Survival analysis showed that there is a significant difference between two groups classified by the genes in module A. Further Univariate Cox regression analysis showed that 72 genes were associated with survival time significantly, such as NPM1, PRKDC, SPARC, HMGA1, COL1A1 and COL1A2.
Nine critical genes related to the pathogenesis and 72 potential prognostic markers were revealed in HCC by the network and module analysis of RNA-seq data. These findings could improve the understanding of the pathogenesis and provide valuable information to further investigate the prognostic markers of HCC.
分析肝细胞癌（HCC）的RNA测序数据，以鉴定与发病机制和预后相关的关键基因。
从基因表达综合数据库（GEO）下载了三个HCC的RNA测序数据集（GSE69164、GSE63863和GSE55758），同时从癌症基因组图谱（TCGA）获得了另一个包含54例有生存时间的HCC病例的数据集。使用R语言的samr包，通过微阵列显著性分析（SAM）方法鉴定差异表达基因（DEG）。随后，我们基于人类蛋白质参考数据库（HPRD）中的信息构建了蛋白质-蛋白质相互作用（PPI）网络。使用CytoScape的插件clusterViz，通过MCODE方法鉴定PPI网络中的模块。使用DAVID进行基因本体（GO）富集分析和通路富集分析。使用survival包，通过Kaplan-Meier（K-M）方法分析生存曲线的差异。
在来自GEO的3个数据集中（GSE69164、GSE63863和GSE55758）共鉴定出2572个DEG。构建的PPI网络包含660个节点和1008条边，网络中揭示了4个模块。发现模块A（包含244个DEG）与HCC密切相关，其基因参与转录因子结合、蛋白质代谢以及细胞凋亡的调控。在模块A中鉴定出9个枢纽基因，包括PRKCA、YWHAZ、KRT18、NDRG1、HSPA1A、HSP90AA1、HSF1、IKGKB和UBE21。该网络提供了这些关键基因的蛋白质-蛋白质相互作用，这些基因与HCC的发病机制有关。生存分析表明，根据模块A中的基因分类的两组之间存在显著差异。进一步的单变量Cox回归分析表明，72个基因与生存时间显著相关，如NPM1、PRKDC、SPARC、HMGA1、COL1A1和COL1A2。
通过对RNA测序数据的网络和模块分析，在HCC中揭示了9个与发病机制相关的关键基因和72个潜在的预后标志物。这些发现可以提高对发病机制的理解，并为进一步研究HCC的预后标志物提供有价值的信息。
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