A Bayesian two-step integrative procedure incorporating prior knowledge for the identification of miRNA-mRNAs involved in hepatocellular carcinoma

Recent studies have confirmed the role of miRNA regulation of gene expression in oncogenesis for various cancers. In parallel, prior knowledge about relationships between miRNA and mRNA have been accumulated from biological experiments or statistical analyses. Improved identification of disease-asso...

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Bibliographic Details
Main Authors: Denis, Marie, Varghese, Rency S., Barefoot, Megan E., Tadesse, Mahlet G., Ressom, Habtom W.
Format: Conference Proceeding
Language:English
Subjects:
Bayesian variable selection
Biological system modeling
Biomarkers
Genomics
graphical models
hepatocellular carcinoma
Input variables
integrative models
Liver
liver cirrhosis prior knowledge
Regulation
Statistical analysis
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Summary:Recent studies have confirmed the role of miRNA regulation of gene expression in oncogenesis for various cancers. In parallel, prior knowledge about relationships between miRNA and mRNA have been accumulated from biological experiments or statistical analyses. Improved identification of disease-associated miRNA-mRNA pairs may be achieved by incorporating prior knowledge into integrative genomic analyses. In this study we focus on 39 patients with hepatocellular carcinoma (HCC) and 25 patients with liver cirrhosis and use a flexible Bayesian two-step integrative method. We found 66 significant miRNA-mRNA pairs, several of which contain molecules that have previously been identified as potential biomarkers. These results demonstrate the utility of the proposed approach in providing a better understanding of relationships between different biological levels, thereby giving insights into the biological mechanisms underlying the diseases, while providing a better selection of biomarkers that may serve as diagnostic, prognostic, or therapeutic biomarker candidates.
ISSN:2375-7477
2694-0604
DOI:10.1109/EMBC48229.2022.9871330