Literature DB >> 21455993

Functional implications of microRNAs in acute myeloid leukemia by integrating microRNA and messenger RNA expression profiling.

Violaine Havelange1, Nicole Stauffer, Catherine C E Heaphy, Stefano Volinia, Michael Andreeff, Guido Marcucci, Carlo M Croce, Ramiro Garzon.   

Abstract

BACKGROUND: The expression of microRNAs (miRNAs) is deregulated in acute myeloid leukemia (AML), but the corresponding functional miRNA-controlled pathways are poorly understood. Integration of messenger RNA (mRNA) and miRNA expression profiling may allow the identification of functional links between the whole transcriptome and microRNome that are involved in myeloid leukemogenesis.
METHODS: We integrated miRNA and mRNA expression profiles obtained from 48 newly diagnosed AML patients by using 2 different microarray platforms and performed correlation, gene ontology, and network analysis. Experimental validation was also performed in AML cell lines using miRNA oligonucleotide mimics and functional assays.
RESULTS: Our analysis identified a strong positive correlation between HOX-related genes and miR-10 and miR-20a. Furthermore, we observed a negative correlation between miR-181a and miR-181b, miR-155, and miR-146 expression with that of genes involved in immunity and inflammation (eg, IRF7 and TLR4) and a positive correlation between miR-23a, miR-26a, miR-128a, and miR-145 expression with that of proapoptotic genes (eg, BIM and PTEN). These correlations were confirmed by gene ontology analyses, which revealed the enrichment of members of the homeobox, immunity and inflammation, and apoptosis biological processes. Furthermore, we validated experimentally the association of miR-145, miR-26a, and miR-128a with apoptosis in AML.
CONCLUSION: Our results indicate that by integrating the transcriptome and microRNome in AML cells, it is possible to identify previously unidentified putative functional miRNA-mRNA interactions in AML.
Copyright © 2011 American Cancer Society.

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Year:  2011        PMID: 21455993      PMCID: PMC3154970          DOI: 10.1002/cncr.26096

Source DB:  PubMed          Journal:  Cancer        ISSN: 0008-543X            Impact factor:   6.860


  39 in total

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  32 in total

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2.  Uncovering low-dimensional, miR-based signatures of acute myeloid and lymphoblastic leukemias with a machine-learning-driven network approach.

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