Open AccessMining cancer gene expression databases for latent information on intronic microRNAs
Author(s) -
Monterisi Simona,
D'Ario Giovanni,
Dama Elisa,
Rotmensz Nicole,
Confalonieri Stefano,
Tordonato Chiara,
Troglio Flavia,
Bertalot Giovanni,
Maisonneuve Patrick,
Viale Giuseppe,
Nicassio Francesco,
Vecchi Manuela,
Di Fiore Pier Paolo,
Bianchi Fabrizio
Publication year - 2015
Publication title -
molecular oncology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.332
H-Index - 88
eISSN - 1878-0261
pISSN - 1574-7891
DOI - 10.1016/j.molonc.2014.10.001
Subject(s) - microrna , gene , computational biology , in silico , biology , gene expression , genetics , identification (biology) , cancer , breast cancer , regulation of gene expression , bioinformatics , botany
Around 50% of all human microRNAs reside within introns of coding genes and are usually co‐transcribed. Gene expression datasets, therefore, should contain a wealth of miRNA‐relevant latent information, exploitable for many basic and translational research aims. The present study was undertaken to investigate this possibility. We developed an in silico approach to identify intronic‐miRNAs relevant to breast cancer, using public gene expression datasets. This led to the identification of a miRNA signature for aggressive breast cancer, and to the characterization of novel roles of selected miRNAs in cancer‐related biological phenotypes. Unexpectedly, in a number of cases, expression regulation of the intronic‐miRNA was more relevant than the expression of their host gene. These results provide a proof of principle for the validity of our intronic miRNA mining strategy, which we envision can be applied not only to cancer research, but also to other biological and biomedical fields.