Identification of microRNA expression patterns and definition of a microRNA/mRNA regulatory network in distinct molecular groups of multiple myeloma

Marta Lionetti(University of Milan), M. Biasiolo(University of Padua), Luca Agnelli(University of Milan), Katia Todoerti(University of Milan), Laura Mosca(University of Milan), Sonia Fabris(University of Milan), Gabriele Sales(University of Padua), Giorgio La Nasa(University of Milan), Silvio Bicciato(University of Modena and Reggio Emilia), Luigia Lombardi(University of Milan), Stefania Bortoluzzi(University of Padua), Antonino Neri(University of Milan)
Blood
October 22, 2009
Cited by 240Open Access
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Abstract

To date, little evidence of miRNA expression/deregulation in multiple myeloma has been reported. To characterize miRNA in the context of the major multiple myeloma molecular types, we generated miRNA expression profiles of highly purified malignant plasma cells from 40 primary tumors. Furthermore, transcriptional profiles, available for all patients, were used to investigate the occurrence of miRNA/predicted target mRNA pair anticorrelations, and the miRNA and genome-wide DNA data were integrated in a subset of patients to evaluate the influence of allelic imbalances on miRNA expression. Differential miRNA expression patterns were identified, which were mainly associated with the major IGH translocations; particularly, t(4;14) patients showed specific overexpression of let-7e, miR-125a-5p, and miR-99b belonging to a cluster at 19q13.33. The occurrence of other lesions (ie, 1q gain, 13q and 17p deletions, and hyperdiploidy) was slightly characterized by specific miRNA signatures. Furthermore, the occurrence of several allelic imbalances or loss of heterozygosity was found significantly associated with the altered expression of miRNAs located in the involved regions, such as let-7b at 22q13.31 or miR-140-3p at 16q22. Finally, the integrative analysis based on computational target prediction and miRNA/mRNA profiling defined a network of putative functional miRNA-target regulatory relations supported by expression data.


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