Evidence map›Paper›PMID 31329578›Full record

ArticlePLoS computational biology2019

Aberrant DNA methylation defines isoform usage in cancer, with functional implications.

Yun-Ching Chen, Laura Elnitski

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.3field-weighted citation impact, top 20% of its field
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

15 citing papers in PubMed, 19 citations in OpenAlex.

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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

2 authors at 1 institution in 1 country.

Yun-Ching ChenGenomic Functional Analysis Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America.ORCID 0000-0002-5342-083X
Laura ElnitskiGenomic Functional Analysis Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America.ORCID 0000-0003-1065-019X
National Institutes of Health · US

Funding

Regulatory and epigenetic landscapes reveal biological processes, disease mechanisms and diagnostic markersZIAHG200323 · NHGRI · NATIONAL HUMAN GENOME RESEARCH INSTITUTE · PI ELNITSKI, LAURA L · 2009 to 2025
$27.6M
6 · The paper itself

Abstract

Alternative transcript isoforms are common in tumors and act as potential drivers of cancer. Mechanisms determining altered isoform expression include somatic mutations in splice regulatory sites or altered splicing factors. However, since DNA methylation is known to regulate transcriptional isoform activity in normal cells, we predicted the highly dysregulated patterns of DNA methylation present in cancer also affect isoform activity. We analyzed DNA methylation and RNA-seq isoform data from 18 human cancer types and found frequent correlations specifically within 11 cancer types. Examining the top 25% of variable methylation sites revealed that the location of the methylated CpG site in a gene determined which isoform was used. In addition, the correlated methylation-isoform patterns classified tumors into known subtypes and predicted distinct protein functions between tumor subtypes. Finally, methylation-correlated isoforms were enriched for oncogenes, tumor suppressors, and cancer-related pathways. These findings provide new insights into the functional impact of dysregulated DNA methylation in cancer and highlight the relationship between the epigenome and transcriptome.

Indexed as

DNA MethylationAlternative SplicingComputational BiologyCpG IslandsFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMaleNeoplasm ProteinsNeoplasmsProtein IsoformsRNA, NeoplasmSequence Analysis, RNATranscription Initiation SiteTranscription Termination, GeneticNeoplasm ProteinsProtein IsoformsRNA, Neoplasm

Identifiers

PMID31329578
PMCPMC6675117
OpenAlexW2964314684

What OpenQuestion holds

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Registered trials

None linked

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.