Statistical Approaches for Hepatocellular Carcinoma (HCC) Biomarker Discovery

Bioinformatics provides an essential tool for the identification of diseases especially human cancer diseases. Also, the availability of the complete human genome has opened the door for the understanding of these diseases as recent technological advances in functional genomics and proteomics have fuelled interest in identifying the biomarkers of complex diseases such as liver cancer which mainly caused to death. Hepatocellular carcinoma (HCC) is one of the common malignant tumours in the world; the liver cirrhosis is the most important leading cause of it. HCC relates to virus infection, carcinogenic compounds, pollution and genetic factors. This work provides a genomic study that focuses on using bioinformatics approaches to predict the molecular causes of HCC by the investigation of the chromosomal aberrations including gain, or loss of the genomic DNA copy number to provide accurate diagnoses of this disease using Comparative genomic hybridization (CGH) arrays. Diagnosis and understanding of the disease processes will provide a potential treatment of the disease at an early stage. The aim is to apply two statistical approaches based on a circular binary segmentation (CBS) algorithm and a Bayesian Hidden Markov Model (HMM) to a number of human chromosomes for analysing array CGH data that accounts for the dependence between neighbouring clones in order to identify genome-wide alternations in copy number from the genomic data. Results provide a well identification of the aberration regions in human chromosomes that may lead to robust biomarkers for the early detection of human HCC.