M-R Yang1, Y Zhang, X-X Wu, W Chen. 1. Department of Hepatopathy,Wuxi No. 5 People's Hospital, Wuxi, China. chendhhs@163.com.
Abstract
OBJECTIVE: RNA-seq data of hepatocellular carcinoma (HCC) was analyzed to identify critical genes related to the pathogenesis and prognosis. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
OBJECTIVE: RNA-seq data of hepatocellular carcinoma (HCC) was analyzed to identify critical genes related to the pathogenesis and prognosis. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.