An Integrative Gene Expression Microarray Meta-analysis Identifies Host Factors and Key Signatures Involved in Hepatitis B Virus Infection

Abstract

Hepatitis B virus (HBV) is a global health concern. Viral and host factors orchestrate the natural history of HBV infection, but the impact of host factors that influence the clinical course of the disease remains poorly understood. The aim of this study was to identify host factors crucial to the HBV life cycle by conducting a meta-analysis utilizing public microarray datasets.

Methods: An integrative meta-analysis of expression data from two microarray datasets of HBVinfected liver tissues and healthy uninfected livers was conducted to identify gene expression signatures and overlapping biological processes modulating infection/disease.

Results: Using integrative meta-analysis of expression data (INMEX), we identified across two datasets a total of 841 genes differentially expressed during HBV infection, including 473 upregulated and 368 downregulated genes. In addition, through functional enrichment and pathway analysis, we observed that Jak-STAT, TLR, and NF-κB are the most relevant signaling pathways in chronic HBV infection. The network-based meta-analysis identified NEDD8, SKP2, JUN, and HIF1A as the most highly ranked hub genes.

Conclusion: Thus, these results may provide valuable information about novel potential host factors modulating chronic HBV infection. Such factors may serve as potential targets for the development of novel therapeutics such as activin receptor-like kinase inhibitors.

Keywords: HBV; expression; gene; gene network; host factors; meta-analysis.