Overexpression of miR-181a regulates the Warburg effect in triple-negative breast cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it