Evidence map›Paper›PMID 41584369›Full record

ArticleF1000Research2023

Epigenetic germline variants predict cancer prognosis and risk and distribute uniquely in topologically associating domains.

Shervin Goudarzi, Meghana Pagadala, Adam Klie, James V Talwar, Hannah Carter

Abstract read
In one paragraph

Article in F1000Research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Shervin GoudarziCanyon Crest Academy, San Diego, California, 92130, USA.
Meghana PagadalaBiomedical Sciences Program, University of California San Diego, La Jolla, California, 92093, USA.
Adam KlieBiomedical Sciences Program, University of California San Diego, La Jolla, California, 92093, USA.
James V TalwarBiomedical Sciences Program, University of California San Diego, La Jolla, California, 92093, USA.
Hannah CarterBioinformatics and Systems Biology Program, University of California San Diego, La Jolla, California, 92093, USA.ORCID 0000-0002-1729-2463

Funding

TR&D 3 - Network Guided Machine LearningP41GM103504 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI IDEKER, TREY · 2012 to 2024
$17.3M
Comprehensive identification of germline-somatic interactionsR01CA269919 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Ludmil B Alexandrov, Hannah Kathryn Carter · 2022 to 2026
$2.2M
NCI NIH HHS R01 CA269919NIGMS NIH HHS P41 GM103504
6 · The paper itself

Abstract

Background: Methylation quantitative trait loci (meQTLs) associate with different levels of local DNA methylation in cancers. Here, we investigated whether the distribution of cancer meQTLs reflected functional organization of the genome in the form of chromatin topologically associated domains (TADs) and evaluated whether cancer meQTLs near known driver genes have the potential to influence cancer risk or progression. Methods: Published cancer meQTLs were analyzed according to their location in transcriptionally active or inactive TADs and TAD boundary regions. Cancer meQTLs near known cancer genes were analyzed for association with cancer risk in the UKBioBank , and prognosis in The Cancer Genome Atlas (TCGA). Results: In TAD boundary regions, the density of cancer meQTLs was higher near inactive TADs. Furthermore, we observed an enrichment of cancer meQTLs in active TADs near tumor suppressors, whereas there was a depletion of such meQTLs near oncogenes. Several meQTLs were associated with cancer risk in the UKBioBank, and we were able to reproduce breast cancer risk associations in the DRIVE cohort. Survival analysis in TCGA implicated a number of meQTLs in 13 tumor types. In 10 of these, polygenic cancer meQTL scores were associated with increased hazard in a CoxPH analysis. Risk and survival-associated meQTLs tended to affect cancer genes involved in DNA damage repair and cellular adhesion and reproduced cancer-specific associations reported in prior literature. Conclusions: This study provides evidence that genetic variants that influence local DNA methylation are affected by chromatin structure and can impact tumor evolution.

Indexed as

Epigenesis, GeneticGerm-Line MutationNeoplasmsDNA MethylationGenetic Predisposition to DiseaseHumansPrognosisQuantitative Trait LociCancerMachine learningmeQTLsPolygenic Risk ScoreTADXGBoost

Identifiers

PMID41584369
PMCPMC12826361

What Socratic holds

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LicenceCC BY
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Registered trials

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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 Socratic graph.