Evidence map›Paper›PMID 41266362›Full record

ArticleNature communications2025

Non-coding genetic variants underlying higher prostate cancer risk in men of African ancestry.

Shan Li, Kaniz Fatema, Nidharshan Sundarraj, Arashdeep Singh, Padma Sheila Rajagopal, Dimple Notani, David Y Takeda, Sridhar Hannenhalli

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Shan LiCancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.ORCID http://orcid.org/0000-0001-6760-0711
Kaniz FatemaGenitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.ORCID http://orcid.org/0009-0002-8552-2846
Nidharshan SundarrajGenetics and Development, National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, Karnataka, India.
Arashdeep SinghCancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.ORCID http://orcid.org/0000-0001-6087-7240
Padma Sheila RajagopalCancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.ORCID http://orcid.org/0000-0002-0489-0819
Dimple NotaniGenetics and Development, National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, Karnataka, India.ORCID http://orcid.org/0000-0002-9460-8070
David Y TakedaGenitourinary Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.ORCID http://orcid.org/0000-0002-5986-1169
Sridhar HannenhalliCancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. sridhar.hannenhalli@nih.gov.ORCID http://orcid.org/0000-0001-9603-7569

Funding

Identification of epigenetic drivers of castration-resistant prostate cancerZIABC011973 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI TAKEDA, DAVID · 2020 to 2025
$4.6M
Identifying non-coding drivers of cancerZIABC011979 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI HANNENHALLI, SRIDHAR · 2020 to 2023
$914k
Intramural NIH HHS ZIA BC011973Intramural NIH HHS ZIA BC011979U.S. Department of Health & Human Services | NIH | Center for Information Technology (Center for Information Technology, National Institutes of Health) 1-ZIA-BC011979-02
6 · The paper itself

Abstract

Prostate cancer (PrCa) incidence and severity vary across ancestries; men of African ancestry (AA) are more likely to be diagnosed and die from PrCa than those of European ancestry (EA). Current polygenic risk scores, even from multi-ancestry GWAS, do not fully capture population-specific genetic mechanisms, especially those mediated by non-coding regulatory single nucleotide polymorphisms (SNPs). Using a deep learning model of prostate enhancers, we identify ~ 2000 SNPs, potentially affecting enhancer function, with higher alternate allele frequency in AA men, that may affect PrCa risk. These SNPs may promote cancer via two mechanisms: increased enhancer activity leading to immune suppression and telomere elongation or decreased activity causing de-differentiation and apoptosis inhibition. Identified SNPs predominantly modulate binding of key transcription factors such as FOX, HOX, and AR - the first was experimentally validated. Incorporating these SNPs into a polygenic risk score improves PrCa risk assessment beyond existing GWAS-identified variants.

Indexed as

Black PeopleGenetic Predisposition to DiseasePolymorphism, Single NucleotideProstatic NeoplasmsDeep LearningEnhancer Elements, GeneticGene FrequencyGenome-Wide Association StudyHumansMaleMultifactorial InheritanceRisk FactorsWhite People

Identifiers

PMID41266362
PMCPMC12635056

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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.