Overexpression of miR-181a regulates the Warburg effect in triple-negative breast cancer
Bibliographic record
Abstract
OBJECTIVE: Triple-negative breast cancer (TNBC) is highly aggressive and leads to a poor prognosis. microRNA-181a (miR-181a) exhibits strong antineoplastic effects in many types of cancer. In this study, we examine the responses of human miR-181a-transfected TNBC cells and explore the mechanisms underlying the observed effects. METHODS: A series of cellular assays were conducted using cells from the MDA-MB-231 TNBC line to assess the impact of miR-181a overexpression. The extracellular acidification rate, lactate production and glucose uptake were evaluated as a measure of aerobic glycolysis (i.e. the Warburg effect). The expressions of glycolysis-related gene were analyzed. RESULTS: Viability, migration and survival of miR-181a-transfected MDA-MB-231 cells were all significantly reduced. miR-181a inhibited glycolysis in TNBC cells by reducing the rates of glucose uptake and lactate production and a substantial downregulation of factors known to contribute to the Warburg effect, including the serine/threonine kinase, AKT3, hypoxia-inducible factor-1α (HIF-1α) and progesterone receptor membrane component 1 (PGRMC1). CONCLUSION: Our results demonstrate that miR-181a may regulate glycolysis in MDA-MB-231 TNBC cells, potentially via interference with components of the AKT3-HIF-1α and PGRMC1 pathways. These results suggest that miR-181a might be developed as a therapeutic agent for use in antineoplastic regimens directed at TNBC and PGRMC1-overexpressing breast cancers.
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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 | 0.001 |
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".