Evidence map›Paper›PMID 28820329›Full record

ArticleRNA biology2017

Sources to variability in circulating human miRNA signatures.

Andreas Keller, Trine Rounge, Christina Backes, Nicole Ludwig, Randi Gislefoss, Petra Leidinger, Hilde Langseth, Eckart Meese

Abstract read
In one paragraph

Article in RNA biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 1 pooled it
–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

33 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Review
  5. Article
  6. Review
  7. Article
  8. Review
  9. Article
  10. Review
  11. Plasma miRNA-146b-3p, -222-3p, -221-5p, -21a-3p Expression Levels andInternational journal of molecular sciences · 2024
    Article
  12. Article
  13. Article
  14. Article
  15. Serum miRNA modulations indicate changes in retinal morphology.Frontiers in molecular neuroscience · 2023
    Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. Article
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

8 authors.

Andreas Kellera Chair for Clinical Bioinformatics , Saarland University , Saarbrücken , Germany.
Trine Roungeb Cancer Registry of Norway , Institute of Population-based Cancer Research , Oslo , Norway.ORCID 0000-0003-2677-2722
Christina Backesa Chair for Clinical Bioinformatics , Saarland University , Saarbrücken , Germany.
Nicole Ludwigc Department of Human Genetics , Saarland University , Homburg , Germany.
Randi Gislefossb Cancer Registry of Norway , Institute of Population-based Cancer Research , Oslo , Norway.
Petra Leidingerc Department of Human Genetics , Saarland University , Homburg , Germany.
Hilde Langsethb Cancer Registry of Norway , Institute of Population-based Cancer Research , Oslo , Norway.
Eckart Meesec Department of Human Genetics , Saarland University , Homburg , Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An increasing number of studies propose circulating microRNAs (miRNAs) as biomarkers for a large number of human diseases including cancer, cardiovascular diseases, neurologic pathologies and others. To further validate miRNA as biomarkers it is indispensable to understand the variability of circulating miRNAs in healthy individuals. We determined the longitudinal miRNomes of 90 serum samples from the Janus Serum Bank in Norway, which have been stored between 23 and 40 y at -25 °Celsius. We profiled 3 serum samples with microarrays for 30 individuals, each. For each individual the samples were collected with a time interval of approximately 5 y. This design allowed insights into inter-individual variability, age dependent miRNA variability and the impact of storage length and pre-processing. A significant proportion of the miRNome was affected by the age of the blood donor and a not negligible, albeit small, part of the miRNome by the storage time. A substantial part of miRNAs was differentially abundant between individuals, independent of the time when samples were collected. Stepwise filtering of the 529 miRNAs that were detected in the serum samples showed 168 miRNAs with differential abundance depending on the time point analyzed, 56 miRNAs differentially abundant between individuals, and 169 miRNAs with an abundance depending on the sampling procedure. While these groups of miRNAs contain generally interesting and biologically important miRNAs, the remaining 135 miRNAs constitute very promising biomarker candidates as they show an overall low variability between healthy individuals, a likewise overall low variability across a longer life span, and a high independence of the sampling process and the storage length.

Indexed as

Circulating MicroRNAAge FactorsBiomarkersBiomarkers, TumorGene Expression ProfilingHumansMicroRNAsNeoplasmsRNA StabilityTranscriptomeBiomarkersBiomarkers, TumorCirculating MicroRNAMicroRNAscirculating biomarkerscontrol signaturesmiRNAserum

Identifiers

PMID28820329
PMCPMC5731815

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.