Evidence map›Paper›PMID 29522551›Full record

ArticlePloS one2018

A high-throughput 3' UTR reporter screening identifies microRNA interactomes of cancer genes.

Gert Van Peer, Evelien Mets, Shana Claeys, Ines De Punt, Steve Lefever, Maté Ongenaert, Pieter Rondou, Frank Speleman, Pieter Mestdagh, Jo Vandesompele

Abstract read
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In one paragraph

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

10 citing papers in PubMed.

  1. Article
  2. Review
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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

10 authors.

Gert Van PeerCenter for Medical Genetics, Department of Pediatrics and Genetics, Ghent University, Ghent, Belgium.ORCID 0000-0001-7597-5521
Evelien MetsCenter for Medical Genetics, Department of Pediatrics and Genetics, Ghent University, Ghent, Belgium.
Shana ClaeysCenter for Medical Genetics, Department of Pediatrics and Genetics, Ghent University, Ghent, Belgium.
Ines De PuntCenter for Medical Genetics, Department of Pediatrics and Genetics, Ghent University, Ghent, Belgium.
Steve LefeverCenter for Medical Genetics, Department of Pediatrics and Genetics, Ghent University, Ghent, Belgium.
Maté OngenaertCenter for Medical Genetics, Department of Pediatrics and Genetics, Ghent University, Ghent, Belgium.
Pieter RondouCenter for Medical Genetics, Department of Pediatrics and Genetics, Ghent University, Ghent, Belgium.
Frank SpelemanCenter for Medical Genetics, Department of Pediatrics and Genetics, Ghent University, Ghent, Belgium.
Pieter MestdaghCenter for Medical Genetics, Department of Pediatrics and Genetics, Ghent University, Ghent, Belgium.
Jo VandesompeleCenter for Medical Genetics, Department of Pediatrics and Genetics, Ghent University, Ghent, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionDespite the established contribution of deregulated microRNA (miRNA) function to carcinogenesis, relatively few miRNA-cancer gene interactions have been validated, making it difficult to appreciate the true complexity of miRNA-cancer gene regulatory networks.

resultsIn this effort, we identify miRNA interactomes of 17 well-established cancer genes, involved in various cancer types, through a miRNome-wide 3' UTR reporter screening. Using a novel and performant strategy for high-throughput screening data analysis, we identify 390 interactions, quadrupling the size of the known miRNA interactome for the cancer genes under investigation. Clear enrichments of established and predicted interactions underscore the validity of the interactome data set. Interactomes appear to be primarily driven by canonical binding site interactions. Nonetheless, non-canonical binding sites, such as offset 6mer and seed-mismatched or G:U wobble sites, also have regulatory activity, albeit clearly less pronounced. Furthermore, we observe enhanced regulation in the presence of 3' supplementary pairing for both canonical and non-canonical binding sites.

conclusionsAltogether, the cancer gene-miRNA interactome data set represents a unique resource that will aid in the unraveling of regulatory miRNA networks and the dynamic regulation of key protein-coding cancer genes. In addition, it uncovers aspects of the functional miRNA binding site's architecture and the relative contributions of different binding site types.

Indexed as

Gene Regulatory NetworksOncogenes3' Untranslated RegionsArea Under CurveBinding SitesDatasets as TopicGenes, ReporterHigh-Throughput Screening AssaysHumansMicroRNAsMolecular Sequence AnnotationMutagenesis, Site-DirectedReproducibility of ResultsROC CurveTransfection3' Untranslated RegionsMicroRNAs

Identifiers

PMID29522551
PMCPMC5844555

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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.