Molecular Mechanism And Biological Network Analysis Of The Progression Of Hepatocarcinogenesis

Liver is an important organ in human body. Hepatocellular carcinoma (HCC) is a major health problem worldwide. It is the sixth most common neoplasm in the world with more than half a million new cases annually, and the main cause of death among cirrhotic patients. Carcinogenesis of HCC is a complex and multistep processes, which is associated with many risk factors. Hepatitis B virus (HBV) and hepatitis C virus (HCV) infections, intakes of alcohol and aflatoxin are widely recognized as the four major etiological factors of HCC. The risk of HCC in patients with chronic hepatitis C is the highest and has been well studied in patients who have established cirrhosis, in whom the incidence of HCC is between 2%-8% per year as reported based on clinic studies. Although previous clinic-based studies revealed close relationships among hepatitis C, cirrhosis and HCC, the underlying molecular mechanism of these phenomena still remains unclear. With the tremendous increase in human protein interaction data, network approach has already been employed to understand molecular mechanisms of diseases, particularly to analyze carcinogenesis and the genotype related to the cancer phenotype. However, most efforts only focused on protein-protein interaction network. Moreover, few of these efforts studied the dynamic network changes during the development and progression of HCC, which will be helpful in understanding the molecular mechanisms of hepatocarcinogenesis. We have integrated multiple data sources at different levels, especially with regard to biological pathways and interaction networks, and built ATP-HL (Atlas of Transcriptomics and Proteomics in Human Liver) database and HCCNet (Hepatocellular carcinoma network) database previously. It lays the foundation for our current work. With the integration of protein-protein interactions(PPIs) data, transcriptional regulatory interactions (TRIs) data and microarray data covering hepatitis C, cirrhosis and Barcelona Clinic Liver Cancer (BCLC) staging HCC, we carried out a dynamic biological network analysis in the progression of HCV induced hepatocarcinogenesis. Finally, we constructed disease-related biological network for each disease stage and identified HCC-risky sub networks that may play roles in the development stage of the corresponding disease and may participate in the following progression of HCV induced hepatocarcinogenesis.