Mining cancer gene expression databases for latent information on intronic microRNAs. Issue 2 (15th October 2014) - 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; et al.
Abstract : 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. Highlights: Publicly available mRNA expression datasets can be used to predict int‐miRNA regulation. We developed an in silico approach to identify int‐miRNAs relevant to cancer. We identified a miRNA signature for aggressive breast cancers. The regulation of int‐miRNA expression may be biologically more relevant than that of their host genes, at least in some cases.