RGD Reference Report - Bioinformatics approach to predict target genes for dysregulated microRNAs in hepatocellular carcinoma: study on a chemically-induced HCC mouse model. - Rat Genome Database

Send us a Message
Submit Data |  Help |  Video Tutorials |  News |  Publications |  Download |  REST API |  Citing RGD |  Contact   
Search RGD Grant Resources Citing RGD About Us Contact Us
Genes Variants Community Projects QTLs Strains Markers Genome Information Ontologies Cell Lines References Download Submit Data
OntoMate (Literature Search) JBrowse (Genome Browser) Synteny Browser (VCMap) Variant Visualizer Multi-Ontology Enrichment (MOET) Gene-Ortholog Location Finder (GOLF) InterViewer (Protein-Protein Interactions) PhenoMiner (Quatitative Phenotypes) Gene Annotator OLGA (Gene List Generator) AllianceMine GViewer (Genome Viewer)
Aging & Age-Related Disease Cancer & Neoplastic Disease Cardiovascular Disease Coronavirus Disease Developmental Disease Diabetes Hematologic Disease Immune & Inflammatory Disease Infectious Disease Liver Disease Neurological Disease Obesity & Metabolic Syndrome Renal Disease Respiratory Disease Sensory Organ Disease
Find Models   new Genetic Models Autism Models Rat PhenoMiner (Quantitative Phenotypes) Chinchilla PhenoMiner Expected Ranges (Quantitative Phenotype) PhenoMiner Term Comparison Hybrid Rat Diversity Panel Phenotypes Phenotypes in Other Animal Models Animal Husbandry Strain Medical Records Phylogenetics Strain Availability Calendar Rats 101 Submissions Photo Archive
Pathways
Rat Community Forum Directory of Rat Laboratories Video Tutorials News RGD Publications RGD Presentations Archive Nomenclature Guidelines Resource Links Laboratory Resources Employment Resources

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

Authors: Del Vecchio, Filippo  Gallo, Francesco  Di Marco, Antinisca  Mastroiaco, Valentina  Caianiello, Pasquale  Zazzeroni, Francesca  Alesse, Edoardo  Tessitore, Alessandra 
Citation: Del Vecchio F, etal., BMC Bioinformatics. 2015 Dec 10;16:408. doi: 10.1186/s12859-015-0836-1.
RGD ID: 153344559
Pubmed: PMID:26652480   (View Abstract at PubMed)
PMCID: PMC4676132   (View Article at PubMed Central)
DOI: DOI:10.1186/s12859-015-0836-1   (Journal Full-text)


BACKGROUND: Hepatocellular 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.
RESULTS: We 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.
CONCLUSIONS: In 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.

RGD Manual Disease Annotations    Click to see Annotation Detail View
TermQualifierEvidenceWithReferenceNotesSourceOriginal Reference(s)
hepatocellular carcinoma  ISOAnk3 (Mus musculus)153344559; 153344559protein:decreased expression:liverRGD 
hepatocellular carcinoma  IEP 153344559protein:decreased expression:liverRGD 

Objects Annotated

Genes (Rattus norvegicus)
Ank3  (ankyrin 3)

Genes (Mus musculus)
Ank3  (ankyrin 3, epithelial)

Genes (Homo sapiens)
ANK3  (ankyrin 3)


Additional Information