Authors: Mai Said Mabrouk
Addresses: Department of Biomedical Engineering, Misr University for Science and Technology (MUST University), Al Motamyez Distinct Al, Al Mehwar Road, 0020, 6th October City, Egypt
Abstract: Hepatitis C is an infection of the liver caused by the hepatitis C virus (HCV). It is a worldwide health problem that damages the liver. The resulting wound-healing process can lead to liver fibrosis and the subsequent development of cirrhosis. In this work, the objective is to find accurate viral biomarkers for liver cancer prediction based on motifs or patterns in the virus C genome using the viral epidemiology signature pattern analysis (VESPA) and entropy estimation. This study was able to classify genomic features from cancerous and non-cancerous virus C patients to extract patterns that can distinguish between two sets of phylogenetic close but functionally different sequences for pattern recognition on the core protein, the E2 protein and their motifs to find new biomarkers for liver cancer prediction. According to results, it is evident that variability is present in both groups of cancerous and non-cancerous virus C patients. Also, it indicated that HCV core gene sequence data might provide useful information about HCC risk and suggest a prospective investigation to establish the relationship between appearance of the viral mutations and development of hepatocellular carcinoma.
Keywords: hepatitis C virus; HCV; liver cancer; hepatic fibrosis; hepatic cirrhosis; Shannon entropy; viral biomarkers; cancer prediction; bioinformatics; pattern recognition; protein motifs; core gene sequences; viral mutations; hepatocellular carcinoma.
International Journal of Medical Engineering and Informatics, 2012 Vol.4 No.3, pp.274 - 281
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 02 Aug 2012 *