MiR-301a-3p Promotes Triple-Negative Breast Cancer Progression via PTEN Suppression in Tumor Cells and the Microenvironment
Bibliographic record
Abstract
Background: Triple-negative breast cancer (TNBC), the most aggressive breast cancer subtype, poses a severe threat to women’s health. Adipose-derived stem cells (ADSCs) and microRNAs (miRNAs) critically influence tumor progression within the tumor microenvironment (TME), but the role of the miR-301a-3p/PTEN axis in TNBC requires elucidation. Methods: PTEN expression and effects were assessed by comparing clinical TNBC tissues with adjacent normal tissues. Mechanisms were investigated using integrated dataset analysis, luciferase reporter assays, functional cell experiments (assessing malignant phenotypes), co-culture models with ADSCs, and in vivo tumor models. Molecular expression (PTEN, vascular endothelial growth factor A (VEGFA)) and pathway activity (phosphoinositide 3-kinase (PI3K)/AKT) were evaluated. Results: MiR-301a-3p was upregulated in TNBC and directly bound PTEN’s 3'-UTR to suppress its expression. Functionally, miR-301a-3p enhanced tumor cell malignancy. Tumor cell-derived exosomes transported miR-301a-3p to ADSCs in the TME, suppressing PTEN, activating the PI3K/AKT pathway, and upregulating VEGFA secretion. In vivo, modulating miR-301a-3p levels significantly altered tumor growth, PTEN expression, and VEGFA production in tumor and peritumoral tissues. Conclusion: MiR-301a-3p drives TNBC progression via exosome-mediated crosstalk with ADSCs, forming a PTEN/PI3K/AKT/VEGFA signaling axis. It represents a promising therapeutic target and novel biomarker with significant clinical value for TNBC treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".