An ensemble feature extraction classifier for the analysis of integrated data of HCV-HCC related DNA microarray

Hepatocellular Carcinoma (HCC) is the one of leading causes of cancer related deaths worldwide. In most cases, the patients are first infected with Hepatitis C virus (HCV) which then progresses to HCC. HCC is usually diagnosed in its advanced stages and is more difficult to treat or cure at this stage. Early diagnosis increases survival rate as treatment options are available for early stages. Therefore, accurate biomarkers of early HCC diagnosis are needed. DNA microarray technology has been widely used in cancer research. Scientists study DNA microarray gene expression data to identify cancer gene signatures which helps in early cancer diagnosis and prognosis. Most studies are done on single data sets and the biomarkers are only fit to work with these data sets. When tested on any other data sets, classification is poor. In this paper, we combined four different data sets of liver tissue samples (100 HCV-cirrhotic tissues and 61 HCV-cirrhotic tissues from patients with HCC). Differently expressed genes were studied by use of high-density oligonucleotide arrays. By analyzing the data, an ensemble feature extraction-classifier was constructed. The classifier was used to distinguish HCV samples from HCV-HCC related samples. We identified a generic gene signature that would predict whether an HCV tissue also infected with HCC or not.