Evidence map›Paper›PMID 37705830›Full record

ArticleNAR genomics and bioinformatics2023

Alternative splicing impacts microRNA regulation within coding regions.

Lena Maria Hackl, Amit Fenn, Zakaria Louadi, Jan Baumbach, Tim Kacprowski, Markus List, Olga Tsoy

Abstract read
In one paragraph

Article in NAR genomics and bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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.

Lena Maria HacklInstitute for Computational Systems Biology, University of Hamburg, Notkestrasse 9, 22607 Hamburg, Germany.
Amit FennInstitute for Computational Systems Biology, University of Hamburg, Notkestrasse 9, 22607 Hamburg, Germany.ORCID https://orcid.org/0000-0003-2203-3922
Zakaria LouadiInstitute for Computational Systems Biology, University of Hamburg, Notkestrasse 9, 22607 Hamburg, Germany.
Jan BaumbachInstitute for Computational Systems Biology, University of Hamburg, Notkestrasse 9, 22607 Hamburg, Germany.ORCID https://orcid.org/0000-0002-0282-0462
Tim KacprowskiDivision Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Rebenring 56, 38106 Braunschweig, Germany.ORCID https://orcid.org/0000-0002-5393-2413
Markus ListChair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Maximus-von-Imhof-Forum 3, 85354 Freising, Germany.ORCID https://orcid.org/0000-0002-0941-4168
Olga TsoyInstitute for Computational Systems Biology, University of Hamburg, Notkestrasse 9, 22607 Hamburg, Germany.ORCID https://orcid.org/0000-0002-7592-2080

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNAs (miRNAs) are small non-coding RNA molecules that bind to target sites in different gene regions and regulate post-transcriptional gene expression. Approximately 95% of human multi-exon genes can be spliced alternatively, which enables the production of functionally diverse transcripts and proteins from a single gene. Through alternative splicing, transcripts might lose the exon with the miRNA target site and become unresponsive to miRNA regulation. To check this hypothesis, we studied the role of miRNA target sites in both coding and non-coding regions using six cancer data sets from The Cancer Genome Atlas (TCGA) and Parkinson's disease data from PPMI. First, we predicted miRNA target sites on mRNAs from their sequence using TarPmiR. To check whether alternative splicing interferes with this regulation, we trained linear regression models to predict miRNA expression from transcript expression. Using nested models, we compared the predictive power of transcripts with miRNA target sites in the coding regions to that of transcripts without target sites. Models containing transcripts with target sites perform significantly better. We conclude that alternative splicing does interfere with miRNA regulation by skipping exons with miRNA target sites within the coding region.

Identifiers

PMID37705830
PMCPMC10495541

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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.