Evidence map›Paper›PMID 40190354›Full record

ReviewMolecular therapy. Oncology2025

miRNA-dependent resistance mechanisms to anti-hormonal therapies in estrogen receptor-positive breast cancer patients.

Zainab Salam Al Hashami, Bert van der Vegt, Marian J E Mourits, Joost Kluiver, Anke van den Berg

Abstract readReview
In one paragraph

Review in Molecular therapy. Oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. 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

5 authors.

Zainab Salam Al HashamiDepartment of Pathology and Medical Biology, University of Groningen, Groningen, the Netherlands.
Bert van der VegtDepartment of Pathology and Medical Biology, University of Groningen, Groningen, the Netherlands.
Marian J E MouritsDepartment of Gynaecological Oncology, University of Groningen, University Medical Centre Groningen, Groningen, the Netherlands.
Joost KluiverDepartment of Pathology and Medical Biology, University of Groningen, Groningen, the Netherlands.
Anke van den BergDepartment of Pathology and Medical Biology, University of Groningen, Groningen, the Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The estrogen receptor (ERα) is expressed in 70%-80% of breast cancers and is a target of endocrine therapy. However, resistance to endocrine therapy poses a significant clinical challenge. MicroRNAs (miRNAs) have emerged as critical players in oncogenesis and as modulators of therapy response. This review provides an overview of miRNAs that modulate anti-hormonal drug responses. We identified 56 miRNAs associated with resistance to endocrine therapy. These miRNAs had a total of 40 proven target genes that were grouped based on their function under currently known resistance mechanisms, including ER modulation, signaling pathway activation, cell-cycle modulation, and other mechanisms. For a limited number of miRNA-target gene interactions, the relevance of the identified target gene(s) was confirmed by copy or rescue of the miRNA-induced phenotype. Overall, this review highlights critical roles of miRNAs as crucial mediators of resistance to anti-hormonal therapy. The identified miRNA-target gene interactions can serve as a foundation for future functional studies exploring the potential of selected miRNAs in overcoming drug resistance, which might improve outcomes for breast cancer patients.

Indexed as

anti-hormonal drug resistanceestrogen receptor-positive breast cancermicroRNAsMT: Regular Issueresistance mechanisms

Identifiers

PMID40190354
PMCPMC11969448

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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.