Xu Zhou1, Hua-Qiang Zhu1, Jun Lu1. 1. Department of General Surgery, Provincial Hospital Affiliated to Shandong University Ji'nan 250014, China.
Abstract
OBJECTIVES: To explore the molecular mechanism of hepatitis B virus-related and hepatitis C virus-related hepatocellular carcinoma, samples from hepatitis B virus and hepatitis C virus infected patients and the normal were compared, respectively. METHODS: In both experiments, genes with high value were selected based on a genome-wide relative significance and genome-wide global significance model. Co-expression network of the selected genes was constructed, and transcription factors in the network were identified. Molecular complex detection algorithm was used to obtain sub-networks. RESULTS: Based on the new model, the top 300 genes were selected. Co-expression network was constructed and transcription factors were identified. We obtained two common genes FCN2 and CXCL14, and two common transcription factors RFX5 and EZH2. In hepatitis B virus experiment, cluster 1 and 3 had the higher value. In cluster 1, ten of the 17 genes and one transcription factor were all reported associated with hepatocellular carcinoma. In cluster 3, transcription factor ESR1 was reported related with hepatocellular carcinoma. In hepatitis C virus experiment, the value of cluster 3 and 4 was higher. In cluster 3, nine genes were reported to play a key role in hepatocellular carcinoma. In cluster 4, there were 5 genes in the 34 genes. To compare the relevance of a node in holding together communicating nodes, centralities based analysis was performed and we obtained some genes with high stress value. CONCLUSION: The analysis above helped us to understand the pathogenesis of hepatitis B virus and hepatitis C virus associated hepatocellular carcinoma.
OBJECTIVES: To explore the molecular mechanism of hepatitis B virus-related and hepatitis C virus-related hepatocellular carcinoma, samples from hepatitis B virus and hepatitis C virus infectedpatients and the normal were compared, respectively. METHODS: In both experiments, genes with high value were selected based on a genome-wide relative significance and genome-wide global significance model. Co-expression network of the selected genes was constructed, and transcription factors in the network were identified. Molecular complex detection algorithm was used to obtain sub-networks. RESULTS: Based on the new model, the top 300 genes were selected. Co-expression network was constructed and transcription factors were identified. We obtained two common genes FCN2 and CXCL14, and two common transcription factors RFX5 and EZH2. In hepatitis B virus experiment, cluster 1 and 3 had the higher value. In cluster 1, ten of the 17 genes and one transcription factor were all reported associated with hepatocellular carcinoma. In cluster 3, transcription factor ESR1 was reported related with hepatocellular carcinoma. In hepatitis C virus experiment, the value of cluster 3 and 4 was higher. In cluster 3, nine genes were reported to play a key role in hepatocellular carcinoma. In cluster 4, there were 5 genes in the 34 genes. To compare the relevance of a node in holding together communicating nodes, centralities based analysis was performed and we obtained some genes with high stress value. CONCLUSION: The analysis above helped us to understand the pathogenesis of hepatitis B virus and hepatitis C virus associated hepatocellular carcinoma.
Entities:
Keywords:
Hepatocellular carcinoma; hepatitis B virus; hepatitis C virus; transcription factor
Authors: Meiqian Sun; Gang Wu; Yao Li; Xuping Fu; Yan Huang; Rong Tang; Yi Guo; Minyan Qiu; Yumin Mao; Feng Zhao; Lin Li; Shengdong Huang; Xianxian Zhao; Yi Xie Journal: Front Biosci (Elite Ed) Date: 2010-06-01