Mining cancer gene expression databases for latent information on intronic microRNAs

Simona Monterisi, Giovanni D'Ario, Elisa Dama, Nicole Rotmensz, Stefano Confalonieri, Chiara Tordonato, Flavia Troglio, Giovanni Bertalot, Patrick Maisonneuve, Giuseppe Viale, Francesco Nicassio, Manuela Vecchi, Pier Paolo Di Fiore, Fabrizio Bianchi

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)473-487
Number of pages15
JournalMolecular Oncology
Issue number2
Publication statusPublished - Feb 1 2015


  • Breast cancer
  • Cancer
  • Gene expression
  • MicroRNA

ASJC Scopus subject areas

  • Cancer Research
  • Genetics
  • Molecular Medicine
  • Medicine(all)


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