Evidence map›Paper›PMID 25026280›Full record

ArticleOncotarget2014

Top associated SNPs in prostate cancer are significantly enriched in cis-expression quantitative trait loci and at transcription factor binding sites.

Junfeng Jiang, Peilin Jia, Bairong Shen, Zhongming Zhao

Open access · diamondAbstract read
In one paragraph

Article in Oncotarget, 2014. 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
3.5field-weighted citation impact, top 8% 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, 33 citations in OpenAlex.

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  8. Identification of expression quantitative trait loci of RPTOR for susceptibility to glioma.Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine · 2016
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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

4 authors at 2 institutions in 2 countries.

Junfeng JiangDepartment of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA; Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China; These authors contribute equally to this work.
Peilin JiaDepartment of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA; Center for Quantitative Sciences, Vanderbilt University, Nashville, TN, USA; These authors contribute equally to this work.
Bairong ShenCenter for Systems Biology, Soochow University, Suzhou, Jiangsu, China.
Zhongming ZhaoDepartment of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA; Center for Quantitative Sciences, Vanderbilt University, Nashville, TN, USA; Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN, USA; Department of Psychiatry, Vanderbilt University School of Medicine, Nashville, TN, USA.
Vanderbilt University · USSoochow University · CN

Funding

Tumor Immunology and Microenvironment Research ProgramP30CA068485 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI MATTHEW J TYSKA · 1995 to 2026
$172.8M
TISSUE CoreP50CA098131 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI PARK, BEN HO, PIETENPOL, JENNIFER A · 2003 to 2024
$48.7M
Understanding and Controlling p120 Dysfunction in CRCP50CA095103 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI COFFEY, ROBERT J. · 2002 to 2017
$33.6M
Mapping the Genetic Architecture of Complex Disease via RNA-seq and GWAS DataR01LM011177 · NLM · VANDERBILT UNIVERSITY · PI WANG, LILY, ZHAO, ZHONGMING · 2012 to 2014
$1.0M
Understanding Genetic Basis of Dental Caries via Integrative Genomic ApproachesR03DE022093 · NIDCR · VANDERBILT UNIVERSITY · PI WANG, LILY, ZHAO, ZHONGMING · 2011 to 2012
$478k
Integrative statistical models for pathway analysis of GWAS dataR21HG006037 · NHGRI · VANDERBILT UNIVERSITY · PI WANG, LILY, ZHAO, ZHONGMING · 2013 to 2014
$445k
NCI NIH HHS P30 CA068485NCI NIH HHS P30CA068485NCI NIH HHS P50 CA095103NCI NIH HHS P50CA095103NCI NIH HHS P50 CA098131NCI NIH HHS P50CA098131NHGRI NIH HHS R21 HG006037NHGRI NIH HHS R21HG006037NIDCR NIH HHS R03 DE022093NIDCR NIH HHS R03DE022093NLM NIH HHS R01 LM011177NLM NIH HHS R01LM011177
6 · The paper itself

Abstract

While genome-wide association studies (GWAS) have revealed thousands of disease risk single nucleotide polymorphisms (SNPs), their functions remain largely unknown. Recent studies have suggested the regulatory roles of GWAS risk variants in several common diseases; however, the complex regulatory structure in prostate cancer is unclear. We investigated the potential regulatory roles of risk variants in two prostate cancer GWAS datasets by their interactions with expression quantitative trait loci (eQTL) and/or transcription factor binding sites (TFBSs) in three populations. Our results indicated that the moderately associated GWAS SNPs were significantly enriched with cis-eQTLs and TFBSs in Caucasians (CEU), but not in African Americans (AA) or Japanese (JPT); this was also observed in an independent pan-cancer related SNPs from the GWAS Catalog. We found that the eQTL enrichment in the CEU population was tissue-specific to eQTLs from CEU lymphoblastoid cell lines. Importantly, we pinpointed two SNPs, rs2861405 and rs4766642, by overlapping results from cis-eQTL and TFBS as applied to the CEU data. These results suggested that prostate cancer associated SNPs and pan-cancer associated SNPs are likely to play regulatory roles in CEU. However, the negative enrichment results in AA or JPT and the potential mechanisms remain to be elucidated in additional samples.

Indexed as

Quantitative Trait LociBinding SitesGenome-Wide Association StudyGenotypeHumansMalePolymorphism, Single NucleotideProstatic NeoplasmsProtein BindingTranscription FactorsTranscription Factors

Identifiers

PMID25026280
PMCPMC4171620
OpenAlexW1590248701

What OpenQuestion holds

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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 OpenQuestion graph.