Bioinformatics approach to predict target genes for dysregulated microRNAs in hepatocellular carcinoma: study on a chemically-induced HCC mouse model

BMC Bioinformatics

Research Article

Valentina Mastroiaco¹  Francesca Zazzeroni¹  Alessandra Tessitore¹  Edoardo Alesse¹  Filippo Del Vecchio¹  Francesco Gallo²  Pasquale Caianiello²  Antinisca Di Marco² 

[1] Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, Via Vetoio, Coppito 2, 67100, L’Aquila, Italy;Department of Computer Engineering and Science, and Mathematics, University of L’Aquila, Via Vetoio, Coppito 1, 67100, L’Aquila, Italy;

关键词: MicroRNA;    hepatocellular carcinoma;    HCC mouse model;    diethylnitrosamine;    DEN;    target prediction;   

DOI  :  10.1186/s12859-015-0836-1

 received in 2015-08-06, accepted in 2015-11-26,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundHepatocellular carcinoma (HCC) is an aggressive epithelial tumor which shows very poor prognosis and high rate of recurrence, representing an urgent problem for public healthcare. MicroRNAs (miRNAs/miRs) are a class of small, non-coding RNAs that attract great attention because of their role in regulation of processes such as cellular growth, proliferation, apoptosis. Because of the thousands of potential interactions between a single miR and target mRNAs, bioinformatics prediction tools are very useful to facilitate the task for individuating and selecting putative target genes. In this study, we present a chemically-induced HCC mouse model to identify differential expression of miRNAs during the progression of the hepatic injury up to HCC onset. In addition, we describe an established bioinformatics approach to highlight putative target genes and protein interaction networks where they are involved.ResultsWe describe four miRs (miR-125a-5p, miR-27a, miR-182, miR-193b) which showed to be differentially expressed in the chemically-induced HCC mouse model. The miRs were subjected to four of the most used predictions tools and 15 predicted target genes were identified. The expression of one (ANK3) among the 15 predicted targets was further validated by immunoblotting. Then, enrichment annotation analysis was performed revealing significant clusters, including some playing a role in ion transporter activity, regulation of receptor protein serine/threonine kinase signaling pathway, protein import into nucleus, regulation of intracellular protein transport, regulation of cell adhesion, growth factor binding, and regulation of TGF-beta/SMAD signaling pathway. A network construction was created and links between the selected miRs, the predicted targets as well as the possible interactions among them and other proteins were built up.ConclusionsIn this study, we combined miRNA expression analysis, obtained by an in vivo HCC mouse model, with a bioinformatics-based workflow. New genes, pathways and protein interactions, putatively involved in HCC initiation and progression, were identified and explored.

【 授权许可】

CC BY   
© Del Vecchio et al. 2015