Abstract
Chronic Infection of Hepatitis B virus (HBV) is one risk factor of hepatocellular carcinoma (HCC). Much effort has been made to research the process of HBV-associated HCC, but its molecular mechanisms of carcinogenesis remain vague. Here, weighted gene co-expression network analysis (WGCNA) was employed to explore the co-expressed modules and hub/key genes correlated to HBV-associated HCC. We found that genes of the most significant module related to HBV-associated HCC were enriched in DNA replication, p53 signaling pathway, cell cycle, and HTLV-1 infection associated pathway; these cellular pathways played critical roles in the initiation and development of HCC or viral infections. Furthermore, seven hub/key genes were identified based on the topological network analysis, and their roles in HCC were verified by expression and Kaplan-Meier survival analysis. Protein-protein interaction and KEGG pathway analysis suggested that these key genes may stimulate cellular proliferation to promote the HCC progression. This study provides new perspectives to the knowledge of the key pathways and genes in the carcinogenesis process of HBV-associated HCC, and our findings provided potential therapeutic targets and clues of the carcinogenesis of HBV-associated HCC.
Highlights
Hepatocellular carcinoma (HCC), which is known as primary liver cancer, is one of the common malignant tumors, and the 2nd leading cause of cancer-related death worldwide [1, 2]
weighted gene co-expression network analysis (WGCNA) groups genes into a module/network according to pairwise correlations between genes of their similar expression profile; these models may correlate to some special clinical traits of interest, such as tumor stages, ages, gender and other biological characteristics or traits that we are interested in
The files were transferred to expression matrix using the RMA algorithm based on R language, including background correction, normalization and summarization. (Supplementary Figure 1: (A) Box plot of relative log expression (RLE) and (B) Box plot of normalized unscaled standard errors (NUSE))
Summary
Hepatocellular carcinoma (HCC), which is known as primary liver cancer, is one of the common malignant tumors, and the 2nd leading cause of cancer-related death worldwide [1, 2]. LncRNAs, cirRNAs), genomic DNAs, and proteins in absolute or relative terms Public databases such as GEO and ArrayExpress store these data and provide information for the research community to reuse [10,11,12], and many data analysis tools and methods have been developed to reanalyze or reuse these data in order to get some new interesting results. Among these methods, weighted gene coexpression network analysis (WGCNA) is a powerful systems biological method to find co-expressed modules and hub/key genes in transcriptomics, proteomic and metabolomic studies [13,14,15]. WGCNA groups genes into a module/network according to pairwise correlations between genes of their similar expression profile; these models may correlate to some special clinical traits of interest, such as tumor stages, ages, gender and other biological characteristics or traits that we are interested in
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