MCT4-driven CAF-mediated metabolic reprogramming in breast cancer microenvironment is a vulnerability targetable by miR-425-5p
Bibliographic record
Abstract
Multiple oncogenic alterations contribute to breast cancer development. Metabolic reprogramming, deeply contributing to tumor microenvironment (TME) education, is now widely recognized as a hallmark of cancer. The reverse Warburg effect induces cancer-associated fibroblasts (CAFs) to produce and secrete L-lactate, enhancing malignant characteristics such as neoangiogenesis, metastatic dissemination, and treatment resistance. Monocarboxylate transporter (MCT) 4 is involved in lactate efflux from CAFs into stromal and epithelial cells. Here, we first assess the expression of miR-425-5p and its target MCT4 in breast cancer CAFs and normal fibroblasts. We analyzed the metabolic changes induced by miR-425-5p in CAFs and its role in the education of breast cancer epithelial cells. We show that miR-425-5p-induced MCT4 knockdown decreased lactate extrusion from CAFs and its availability in the TME. miR-425-5p overexpression induced profound metabolic transformation in CAFs, ultimately influencing breast cancer metabolism. Furthermore, miR-425-5p impaired the capacity of CAFs to sustain vessel formation and breast cancer cell migration, viability, and proliferation. These findings emphasize the key role of miR-425-5p in breast cancer metabolism and aggressiveness, and its possible importance for breast cancer therapy and monitoring.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".