Mining of Gene Modules and Identification of Key Genes in Hepatocellular Carcinoma based on Gene Co-expression Network Analysis

About 80% of liver cancer cases were hepatocellular carcinoma. To explore the pathogenesis of hepatocellular carcinoma, a bioinformatics algorithm based on gene co-expression network analysis was used to study the gene expression data of hepatocellular carcinoma in this paper. The Pearson correlation analysis was used to construct the 2538 genes into a gene co-expression network, and the eigenvector algorithms was used to divide genes into 9 modules. The correlation analysis between gene modules and clinical indicators results showed that the RNA localization (GO: 0006403) related genes changed in four modules. The cell cycle and mitosis processes were related to event module, tRNA transport and multi-organism transport processes were related to T module, Organic biosynthetic process was related to N module and viral transcription process was related to M module. Furthermore, the Disgenet database results showed that 6 key genes was related to liver cancer, such as CASP2, HCFC1, ILF3, NAA40, NCOA6 and SENP1. Among them, the expression of CASP2, ILF3, NAA40 and NCOA6 were negatively correlated with the survival prognosis. Thus, these identified genes may play important roles in the progression of hepatocellular carcinoma and sever as potential biomarker for future diagnosis.