Evidence map›Paper›PMID 41071767›Full record

ArticlePloS one2025

Identification and evaluation of potential microRNA markers for diagnostics in neurodegenerative diseases and correlation with other biochemical markers.

Richard Novobilský, Pavlína Kušnierová, Dominik Štěpán, Petra Bártová, David Stejskal, Michal Bar

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Richard NovobilskýClinic of Neurology, University Hospital Ostrava, Ostrava, Czech Republic.ORCID https://orcid.org/0009-0000-7326-6035
Pavlína KušnierováDepartment of Clinical Biochemistry, Institute of Laboratory Medicine, University Hospital Ostrava, Ostrava, Czech Republic.ORCID https://orcid.org/0000-0001-8835-2155
Dominik ŠtěpánDepartment of Clinical Biochemistry, Institute of Laboratory Medicine, University Hospital Ostrava, Ostrava, Czech Republic.
Petra BártováClinic of Neurology, University Hospital Ostrava, Ostrava, Czech Republic.ORCID https://orcid.org/0000-0002-4191-9561
David StejskalDepartment of Clinical Biochemistry, Institute of Laboratory Medicine, University Hospital Ostrava, Ostrava, Czech Republic.
Michal BarClinic of Neurology, University Hospital Ostrava, Ostrava, Czech Republic.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesMicroRNAs (miRNA) are short, non-coding RNA molecules that play a crucial role in the development of organisms and are involved in various biological processes. They are considered potential biomarkers for many diseases, including neurodegenerative diseases. This study aimed to identify a set of microRNA targets that exhibited the greatest potential in successfully distinguishing and differentiating neurodegenerative diseases and to establish a correlation between selected miRNAs across different diagnostic groups.

methodsThe study included the analysis of 126 patients. The patients were divided into five diagnostic groups - Alzheimer's disease, non-Alzheimer's dementia, Movement disorder, Dementia and movement disorder, and Healthy controls. The circulating RNA was isolated using the iCatcher Circulating cfRNA 1000 Kit with the iCatcher 12 automated isolator. The determination of microRNA was performed by TT-qPCR in the CFX96™ Real-Time Detection System. The concentrations of the remaining biomarkers were determined by ELISA. The statistical data were processed using MS Excel and MedCalc® software.

resultsThe following miRNAs were studied based on the primary screen for identification of potential microRNA targets and published literature data:hsa-miR-23a-3p, hsa-miR-29c-3p, hsa-miR-30b-5p, hsa-miR-142a-5p, hsa-miR-146a-5p, hsa-miR-151a-3p.A statistically significant correlation was identified between hsa-miR-29c-3p and hsa-miR-30b-5p, hsa-miR-30b-5p and hsa-miR-151a-3p, hsa-miR-23a-3p and hsa-miR-29c-3p, hsa-miR-23a-3p and hsa-miR-151a-3p, between hsa-miR23a-3p and hsa-miR-30b-5p, between hsa-miR-142a-5p and hsa-miR-146a-5p, hsa-miR-142a-5p and hsa-miR-151a-3p as well as between hsa-miR-146a-5p and hsa-miR-151a-3p.Significant differences were observed in hsa-miR-23a-3p and hsa-miR-29c-3p among different diagnostic groups. Compared to classical biomarkers of dementia, significant correlations were observed between plasmatic amyloid-β peptide 42 and hsa-miR-29c-3p, hsa-miR-142a-5p, hsa-miR-146a-5p, hsa-miR-151a-3p. Similar correlations were also found with the plasmatic amyloid-β peptide ratio of 42/40.

conclusionsThe most promising microRNAs for differentiating among neurodegenerative diseases are hsa-miR-23a-3p and hsa-miR-29c-3p. Additionally, there is a correlation between hsa-miR-29c-3p and amyloid-β peptide and the ratio of amyloid-β peptide 42/40.While more robust studies are necessary, there could be a potential for utilizing this miRNA as a therapeutic agent in the future.

Indexed as

MicroRNAsNeurodegenerative DiseasesAgedAlzheimer DiseaseBiomarkersCase-Control StudiesFemaleHumansMaleMiddle AgedBiomarkersMicroRNAs

Identifiers

PMID41071767
PMCPMC12513651

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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.