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National Institute of Environmental Health Sciences

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Publication Detail

Title: Inferred miRNA activity identifies miRNA-mediated regulatory networks underlying multiple cancers.

Authors: Lee, Eunjee; Ito, Koichi; Zhao, Yong; Schadt, Eric E; Irie, Hanna Y; Zhu, Jun

Published In Bioinformatics, (2016 Jan 01)

Abstract: MOTIVATION: MicroRNAs (miRNAs) play a key role in regulating tumor progression and metastasis. Identifying key miRNAs, defined by their functional activities, can provide a deeper understanding of biology of miRNAs in cancer. However, miRNA expression level cannot accurately reflect miRNA activity. RESULTS: We developed a computational approach, ActMiR, for identifying active miRNAs and miRNA-mediated regulatory mechanisms. Applying ActMiR to four cancer datasets in The Cancer Genome Atlas (TCGA), we showed that (i) miRNA activity was tumor subtype specific; (ii) genes correlated with inferred miRNA activities were more likely to enrich for miRNA binding motifs; (iii) expression levels of these genes and inferred miRNA activities were more likely to be negatively correlated. For the four cancer types in TCGA we identified 77-229 key miRNAs for each cancer subtype and annotated their biological functions. The miRNA-target pairs, predicted by our ActMiR algorithm but not by correlation of miRNA expression levels, were experimentally validated. The functional activities of key miRNAs were further demonstrated to be associated with clinical outcomes for other cancer types using independent datasets. For ER(-)/HER2(-) breast cancers, we identified activities of key miRNAs let-7d and miR-18a as potential prognostic markers and validated them in two independent ER(-)/HER2(-) breast cancer datasets. Our work provides a novel scheme to facilitate our understanding of miRNA. In summary, inferred activity of key miRNA provided a functional link to its mediated regulatory network, and can be used to robustly predict patient's survival. AVAILABILITY AND IMPLEMENTATION: the software is freely available at http://research.mssm.edu/integrative-network-biology/Software.html. CONTACT: jun.zhu@mssm.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PubMed ID: 26358730 Exiting the NIEHS site

MeSH Terms: No MeSH terms associated with this publication

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