Evidence map›Paper›PMID 26020509›Full record

Trial reportPloS one2015

MiRNA Profiles in Lymphoblastoid Cell Lines of Finnish Prostate Cancer Families.

Daniel Fischer, Tiina Wahlfors, Henna Mattila, Hannu Oja, Teuvo L J Tammela, Johanna Schleutker

Open access · goldAbstract readClinical TrialMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Trial report in PloS one, 2015. 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
0.4field-weighted citation impact, top 37% 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, 9 citations in OpenAlex.

  1. Article
  2. Minor Intron Splicing from Basic Science to Disease.International journal of molecular sciences · 2021
    Review
  3. Article
  4. The Glucose-RegulatedCancers · 2018
    Review
  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

6 authors at 3 institutions in 1 country.

Daniel FischerSchool of Health Sciences, University of Tampere, 33014 Tampere, Finland.
Tiina WahlforsBioMediTech, University of Tampere, and Fimlab Laboratories, Tampere, Finland.
Henna MattilaBioMediTech, University of Tampere, and Fimlab Laboratories, Tampere, Finland.
Hannu OjaDepartment of Mathematics and Statistics, University of Turku, 20014 Turku, Finland.
Teuvo L J TammelaDepartment of Urology, Tampere University Hospital and Medical School, University of Tampere, Tampere, Finland.
Johanna SchleutkerMedical Biochemistry and Genetics, Institute of Biomedicine, University of Turku, Turku, Finland.
Tampere University · FIUniversity of Turku · FITampere University Hospital · FI

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHeritable factors are evidently involved in prostate cancer (PrCa) carcinogenesis, but currently, genetic markers are not routinely used in screening or diagnostics of the disease. More precise information is needed for making treatment decisions to distinguish aggressive cases from indolent disease, for which heritable factors could be a useful tool. The genetic makeup of PrCa has only recently begun to be unravelled through large-scale genome-wide association studies (GWAS). The thus far identified Single Nucleotide Polymorphisms (SNPs) explain, however, only a fraction of familial clustering. Moreover, the known risk SNPs are not associated with the clinical outcome of the disease, such as aggressive or metastasised disease, and therefore cannot be used to predict the prognosis. Annotating the SNPs with deep clinical data together with miRNA expression profiles can improve the understanding of the underlying mechanisms of different phenotypes of prostate cancer.

resultsIn this study microRNA (miRNA) profiles were studied as potential biomarkers to predict the disease outcome. The study subjects were from Finnish high risk prostate cancer families. To identify potential biomarkers we combined a novel non-parametrical test with an importance measure provided from a Random Forest classifier. This combination delivered a set of nine miRNAs that was able to separate cases from controls. The detected miRNA expression profiles could predict the development of the disease years before the actual PrCa diagnosis or detect the existence of other cancers in the studied individuals. Furthermore, using an expression Quantitative Trait Loci (eQTL) analysis, regulatory SNPs for miRNA miR-483-3p that were also directly associated with PrCa were found.

conclusionBased on our findings, we suggest that blood-based miRNA expression profiling can be used in the diagnosis and maybe even prognosis of the disease. In the future, miRNA profiling could possibly be used in targeted screening, together with Prostate Specific Antigene (PSA) testing, to identify men with an elevated PrCa risk.

Indexed as

FamilyGene Expression Regulation, NeoplasticMicroRNAsPolymorphism, Single NucleotideProstatic NeoplasmsRNA, NeoplasmAdultAgedAged, 80 and overCell LineFinlandGene Expression ProfilingHumansMaleMiddle AgedNeoplasm MetastasisMicroRNAsMIRN483 microRNA, humanRNA, Neoplasm

Identifiers

PMID26020509
PMCPMC4447459
OpenAlexW2256502871

What OpenQuestion holds

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LicenceCC BY
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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.