Evidence map›Paper›PMID 35432445›Full record

ArticleFrontiers in genetics2022

A microRNA Transcriptome-wide Association Study of Prostate Cancer Risk.

Nicholas B Larson, Shannon K McDonnell, Zachary Fogarty, Yuanhang Liu, Amy J French, Lori S Tillmans, John C Cheville, Liang Wang, Daniel J Schaid, Stephen N Thibodeau

Open access · goldAbstract read
In one paragraph

Article in Frontiers in genetics, 2022. 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
0.6field-weighted citation impact, top 41% 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

5 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Review
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

10 authors at 3 institutions in 1 country.

Nicholas B LarsonDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States.
Shannon K McDonnellDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States.
Zachary FogartyDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States.
Yuanhang LiuDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States.
Amy J FrenchDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States.
Lori S TillmansDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States.
John C ChevilleDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States.
Liang WangDepartment of Tumor Biology, H. Lee Moffitt Cancer Center, Tampa, FL, United States.
Daniel J SchaidDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States.
Stephen N ThibodeauDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States.
Mayo Clinic in Florida · USMayo Clinic in Arizona · USMoffitt Cancer Center · US

Funding

Women's Cancer ProgramP30CA015083 · NCI · MAYO CLINIC ROCHESTER · PI Felicity Enders · 1985 to 2026
$151.3M
Genetic determinants of gene expression phenotypes in aggressive prostate cancerR01CA157881 · NCI · MEDICAL COLLEGE OF WISCONSIN · PI WANG, LIANG · 2011 to 2016
$2.9M
NCI NIH HHS N01 CA015083NCI NIH HHS P30 CA015083NCI NIH HHS R01 CA157881
6 · The paper itself

Abstract

Large genome-wide association studies have identified hundreds of single-nucleotide polymorphisms associated with increased risk of prostate cancer (PrCa), and many of these risk loci is presumed to confer regulatory effects on gene expression. While eQTL studies of long RNAs has yielded many potential risk genes, the relationship between PrCa risk genetics and microRNA expression dysregulation is understudied. We performed an microRNA transcriptome-wide association study of PrCa risk using small RNA sequencing and genome-wide genotyping data from N = 441 normal prostate epithelium tissue samples along with N = 411 prostate adenocarcinoma tumor samples from the Cancer Genome Atlas (TCGA). Genetically regulated expression prediction models were trained for all expressed microRNAs using the FUSION TWAS software. TWAS for PrCa risk was performed with both sets of models using single-SNP summary statistics from the recent PRACTICAL consortium PrCa case-control OncoArray GWAS meta-analysis. A total of 613 and 571 distinct expressed microRNAs were identified in the normal and tumor tissue datasets, respectively (overlap: 480). Among these, 79 (13%) normal tissue microRNAs demonstrated significant cis-heritability (median cis-h2 = 0.15, range: 0.03-0.79) for model training. Similar results were obtained from TCGA tumor samples, with 48 (9%) microRNA expression models successfully trained (median cis-h2 = 0.14, range: 0.06-0.60). Using normal tissue models, we identified two significant TWAS microRNA associations with PrCa risk: over-expression of mir-941 family microRNAs (P

Indexed as

eQTLexpressionmicroRNAprostate cancerTWAS

Identifiers

PMID35432445
PMCPMC9006872
OpenAlexW4220681229

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.