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

Denis Marie, Varghese Rency S., Barefoot Megan E., Tadesse Mahlet, Ressom Habtom W.. 2022. A Bayesian two-step integrative procedure incorporating prior knowledge for the identification of miRNA-mRNAs involved in hepatocellular carcinoma. In : 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE. New-York : IEEE, 81-86. ISBN 9781728127835 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 44, Glasgow, Royaume-Uni, 11 Juillet 2022/15 Juillet 2022.

https://doi.org/10.1109/EMBC48229.2022.9871330

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Résumé : 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.

Mots-clés libres : Multi -omic, Bayeisan hierarchical, Hepatocellular carcinoma

Agences de financement européennes : European Commission

Programme de financement européen : H2020

Projets sur financement : (EU) Innovative Statistical modelling for a better Understanding of Longitudinal multivariate responses in relation to Omic datasets

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