Evidence map›Paper›PMID 27852308›Full record

ArticleMolecular cancer2016

Systems analysis identifies miR-29b regulation of invasiveness in melanoma.

Miles C Andrews, Joseph Cursons, Daniel G Hurley, Matthew Anaka, Jonathan S Cebon, Andreas Behren, Edmund J Crampin

Abstract read
In one paragraph

Article in Molecular cancer, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

  1. Review
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  3. Regulatory Mechanisms of SPARC Overexpression in Melanoma Progression.International journal of molecular sciences · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Miles C AndrewsOlivia Newton-John Cancer Research Institute, Heidelberg, VIC, 3084, Australia.
Joseph CursonsSystems Biology Laboratory, University of Melbourne, Parkville, VIC, 3010, Australia.
Daniel G HurleySystems Biology Laboratory, University of Melbourne, Parkville, VIC, 3010, Australia.
Matthew AnakaLudwig Institute for Cancer Research, Melbourne-Austin Branch, Cancer Immunobiology Laboratory, Heidelberg, VIC, 3084, Australia.
Jonathan S CebonOlivia Newton-John Cancer Research Institute, Heidelberg, VIC, 3084, Australia. jonathan.cebon@onjcri.org.au.
Andreas BehrenOlivia Newton-John Cancer Research Institute, Heidelberg, VIC, 3084, Australia. andreas.behren@onjcri.org.au.
Edmund J CrampinDepartment of Medicine, University of Melbourne, Parkville, VIC, 3010, Australia. edmund.crampin@unimelb.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn 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.

methodsWe 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.

resultsSeveral 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.

conclusionsThis 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: http://github.com/uomsystemsbiology/LMMEL-miR-miner.

Indexed as

Gene Expression Regulation, NeoplasticAlgorithmsCell Line, TumorCell MovementComputational BiologyDisease ProgressionGene Expression ProfilingHumansMelanomaMicroRNAsNeoplasm InvasivenessPhenotypeRNA InterferenceRNA, MessengerRNA Processing, Post-TranscriptionalRNA StabilityMicroRNAsMIRN29a microRNA, humanRNA, MessengerCancerDIANA-microT CDSMelanomamicro-RNAPhenotypic switchingStatistical associationSystems biologyTargetScan

Identifiers

PMID27852308
PMCPMC5112703

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.