Analysis of aberrant pathways using HCC candidate biomarkers identified from high-throuput omics studies

Various omics studies have led to identification of a large number of disease-associated biomarkers at different molecular levels (e.g. DNA, mRNA, protein, metabolites). For example, hundreds of candidate biomarkers of human hepatocellular carcinoma (HCC) have been reported in the literature. Howeve...

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Bibliographic Details
Main Authors: Jinlian Wang, Ressom, H. W.
Format: Conference Proceeding
Language:English
Subjects:
Bioinformatics
Biomarkers
Cancer
Diseases
high-througput omics
human hepatocellular carcinoma (HCC)
Humans
pathways
Proteins
Text mining
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Summary:Various omics studies have led to identification of a large number of disease-associated biomarkers at different molecular levels (e.g. DNA, mRNA, protein, metabolites). For example, hundreds of candidate biomarkers of human hepatocellular carcinoma (HCC) have been reported in the literature. However, the molecular mechanism and etiology of HCC have not been adequately determined. In this study, we investigate aberrant key pathways that may be altered in the progression of HCC by using text-mining and pathway centric analysis of diverse biomarkers identified from previous omics studies. The results show that Wnt/β-catenin signaling is the most significantly enriched pathway in these biomarkers. Also, we observe that cancer and gastrointestinal disease are the two most significant biological functions associated with these biomarkers. We believe that integration of candidate HCC biomarkers through text-mining and pathway-centric approaches help improve our understanding of the molecular pathogenesis of HCC and identify key drug targets.
DOI:10.1109/BIBMW.2011.6112552