Evidence map›Paper›PMID 42882513›Full record

ReviewCells2026

Aberrant Transcription-Factor Expression and Regulatory microRNAs in Metastatic Breast Cancer.

Eman Hillal Althubaiti

Abstract readReview
In one paragraph

Review in Cells, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

1 author.

Eman Hillal AlthubaitiDepartment of Biotechnology, College of Science, Taif University, Taif 21944, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metastatic breast cancer is driven by reversible cell-state programs shaped by transcription factors (TFs) and microRNAs (miRNAs). Dysregulated TFs integrate oncogenic signaling, hypoxia, inflammation, lineage identity, mechanical cues, and chromatin state, whereas miRNAs buffer or reinforce these programs through post-transcriptional repression. Their reciprocal interactions form dynamic regulatory circuits controlling epithelial-mesenchymal plasticity, stem-like behavior, invasion, dissemination, dormancy, organ-specific colonization, immune escape, and treatment resistance. This narrative review examines major TF-miRNA modules implicated in breast cancer metastasis, including ZEB1/2-miR-200/miR-205, TWIST1-miR-10b, MYC-miR-9, FRA-1-miR-34, FOXM1-miR-671-5p, HIF-1α-miR-18a, mutant p53/HIF-1α-miR-30d, STAT3-miR-21/miR-34a, and the context-dependent FOXP3-miR-200c/miR-141 network. Evidence from experimental models, paired primary-metastatic tissues, circulating tumor cells, extracellular vesicles, and plasma cohorts is integrated with emphasis on disease stage, molecular subtype, and biological compartment. Tissue and circulating miRNA signals are not interchangeable, and FOXP3-miR-200c relationships appear compartment- and model-dependent. Clinical translation remains limited by tumor heterogeneity, pre-analytical variability, incomplete cellular resolution, and limited causal validation. Prospective, subtype-stratified studies integrating spatial, single-cell, metastatic-tissue, and standardized liquid-biopsy analyses are required before TF-miRNA signatures can guide clinical management.

Indexed as

Breast NeoplasmsGene Expression Regulation, NeoplasticMicroRNAsTranscription FactorsAnimalsFemaleHumansNeoplasm MetastasisMicroRNAsTranscription Factorsbreast cancercirculating biomarkersepithelial–mesenchymal plasticityFOXP3metastasismicroRNAsmiR-200ctranscription factorsZEB1

Identifiers

PMID42882513
PMCPMC13664331

What OpenQuestion holds

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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.