Analysis of protein interaction networks for the detection of candidate hepatitis B and C biomarkers

Abstract

Hepatitis B virus (HBV) and hepatitis C virus (HCV) infection are the major causes of chronic liver disease, cirrhosis and hepatocellular carcinoma (HCC). The resolution or chronicity of acute infection is dependent on a complex interplay between virus and innate/adaptive immunity. The mechanisms that lead a significant proportion of patients to more severe liver disease are not clearly defined and involve virus induced host gene/protein alterations. The utilization of protein interaction networks (PINs) is expected to identify novel aspects of the disease concerning the patients' immune response to virus as well as the main pathways that are involved in the development of fibrosis and HCC. In this study, we designed several PINs for HBV and HCV and employed topological, modular, and functional analysis techniques in order to determine significant network nodes that correspond to prominent candidate biomarkers. The networks were built using data from various interaction databases. When the overall PINs of HBV and HCV were compared, 48 nodes were found in common. The implementation of a statistical ranking procedure indicated that three of them are of higher importance.