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Title: Systems analysis identifies miR-29b regulation of invasiveness in melanoma
Austin Authors: Andrews, Miles C;Cursons, Joseph;Hurley, Daniel G;Anaka, Matthew;Cebon, Jonathan S ;Behren, Andreas;Crampin, Edmund J
Affiliation: Olivia Newton-John Cancer Research Institute, Heidelberg, Victoria, Australia
Ludwig Institute for Cancer Research, Melbourne-Austin Branch, Cancer Immunobiology Laboratory, Heidelberg, Victoria, Australia
School of Cancer Medicine, La Trobe University, Heidelberg, Victoria, Australia
Department of Medicine, University of Melbourne, Parkville, Victoria, Australia
Systems Biology Laboratory, University of Melbourne, Parkville, Victoria, Australia
ARC Centre of Excellence in Convergent Bio-Nano Science, University of Melbourne, Parkville, Victoria, Australia
School of Mathematics and Statistics, University of Melbourne, Parkville, Victoria, Australia
Centre for Systems Genomics, University of Melbourne, Parkville, Victoria, Australia
Department of Medicine, University of Toronto, Toronto, Ontario, Canada
Issue Date: 16-Nov-2016
Date: 2016-11-16
Publication information: Molecular Cancer 2016; 15(1): 72
Abstract: BACKGROUND: In many cancers, microRNAs (miRs) contribute to metastatic progression by modulating phenotypic reprogramming processes such as epithelial-mesenchymal plasticity. This can be driven by miRs targeting multiple mRNA transcripts, inducing regulated changes across large sets of genes. The miR-target databases TargetScan and DIANA-microT predict putative relationships by examining sequence complementarity between miRs and mRNAs. However, it remains a challenge to identify which miR-mRNA interactions are active at endogenous expression levels, and of biological consequence. METHODS: We developed a workflow to integrate TargetScan and DIANA-microT predictions into the analysis of data-driven associations calculated from transcript abundance (RNASeq) data, specifically the mutual information and Pearson's correlation metrics. We use this workflow to identify putative relationships of miR-mediated mRNA repression with strong support from both lines of evidence. Applying this approach systematically to a large, published collection of unique melanoma cell lines - the Ludwig Melbourne melanoma (LM-MEL) cell line panel - we identified putative miR-mRNA interactions that may contribute to invasiveness. This guided the selection of interactions of interest for further in vitro validation studies. RESULTS: Several miR-mRNA regulatory relationships supported by TargetScan and DIANA-microT demonstrated differential activity across cell lines of varying matrigel invasiveness. Strong negative statistical associations for these putative regulatory relationships were consistent with target mRNA inhibition by the miR, and suggest that differential activity of such miR-mRNA relationships contribute to differences in melanoma invasiveness. Many of these relationships were reflected across the skin cutaneous melanoma TCGA dataset, indicating that these observations also show graded activity across clinical samples. Several of these miRs are implicated in cancer progression (miR-211, -340, -125b, -221, and -29b). The specific role for miR-29b-3p in melanoma has not been well studied. We experimentally validated the predicted miR-29b-3p regulation of LAMC1 and PPIC and LASP1, and show that dysregulation of miR-29b-3p or these mRNA targets can influence cellular invasiveness in vitro. CONCLUSIONS: This analytic strategy provides a comprehensive, systems-level approach to identify miR-mRNA regulation in high-throughput cancer data, identifies novel putative interactions with functional phenotypic relevance, and can be used to direct experimental resources for subsequent experimental validation. Computational scripts are available:
DOI: 10.1186/s12943-016-0554-y
Journal: Molecular Cancer
PubMed URL:
Type: Journal Article
Subjects: Cancer
Phenotypic switching
Statistical association
Systems biology
Appears in Collections:Journal articles

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