Evidence map›Paper›PMID 40704027›Full record

ArticleMolecular therapy. Nucleic acids2025

Exploring potent miRNA combinations for detecting early-stage breast cancer.

Barbara N Borsos, Zoltán G Páhi, Tibor Pankotai

Abstract read
In one paragraph

Article in Molecular therapy. Nucleic acids, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

3 authors.

Barbara N BorsosHungarian Centre of Excellence for Molecular Medicine (HCEMM), Genome Integrity and DNA Repair Core Group, Budapesti út 9, 6728 Szeged, Hungary.
Zoltán G PáhiHungarian Centre of Excellence for Molecular Medicine (HCEMM), Genome Integrity and DNA Repair Core Group, Budapesti út 9, 6728 Szeged, Hungary.
Tibor PankotaiHungarian Centre of Excellence for Molecular Medicine (HCEMM), Genome Integrity and DNA Repair Core Group, Budapesti út 9, 6728 Szeged, Hungary.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer (BC) is the most prevalent cancer among women globally. Identifying BC at its early stage is of significant importance; however, due to the limited resolution and the accompanying adverse events of the current detection methods, including mammography, in clinical practice, it remains challenging. Minimally invasive screening would be a promising solution to overcome these constraints. In the current study, we identified serum microRNAs (miRNAs) that could serve as potential targets for recognizing early-stage BC. We quantitatively measured the expression changes of 14 miRNAs in the blood sera of our study cohort that enrolled 59 patients with BC and 42 healthy volunteers. Subsequently, we selected the most prominent correlated multiple miRNAs that can discriminate well between BC patients and healthy subjects. Moreover, we supported the relevance of our findings using an external validation dataset, including 115 BC patients and 81 healthy individuals. Nonetheless, our study unveiled that implementing the regular monitoring of miR-96+miR-15a+miR-16, miR-96+miR-195+miR-15a+miR-16, and miR-96+miR-124+miR-183+miR-195+miR-15a+miR-16 expressions in the accepted screening methods would help recognize early-stage BC and contribute to more effective BC management. Since it will require more extensive study in the future, it cannot replace current imaging methods; however, it can improve the detection accuracy of BC.

Indexed as

biomarkerblood serabreast cancerdiagnostic potentialminimally invasivemiRNAmolecular diagnosticsMT: Non-coding RNAsserum diagnostics

Identifiers

PMID40704027
PMCPMC12284516

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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.