MicroRNA-181a suppresses norethisterone-promoted tumorigenesis of breast epithelial MCF10A cells through the PGRMC1/EGFR–PI3K/Akt/mTOR signaling pathway
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Bibliographic record
Abstract
BACKGROUND: Research suggests that hormone replacement therapy may increase the risk of breast cancer, and progestins such as norethisterone (NET) play a key role in this phenomenon. We have demonstrated that microRNA-181a (miR-181a) suppresses NET-promoted breast cancer cell survival. Nonetheless, the effects of NET and miR-181a on the tumorigenesis of human breast epithelial cells have not yet been elaborated. METHODS: Assays of cell viability, proliferation, migration, apoptosis, and colony formation were performed to investigate the pro-tumorigenesis effect of NET and the effects of miR-181a on human breast epithelial MCF10A cells. The expressions of cell-proliferation-related genes and apoptotic factors were analyzed by quantitative RT-PCR and Western blot in MCF10A cells treated with NET and miR-181a. RESULTS: NET significantly increased MCF10A cell viability, proliferation, migration, and colony formation, but reduced cellular apoptosis. In addition, NET increased the expression of progesterone receptor membrane component 1 (PGRMC1), EGFR, B-cell lymphoma 2, cyclin D1, and proliferating cell nuclear antigen, but decreased the expression of pro-apoptosis factors, such as Bax, caspase-7, and caspase-9. Overexpression of miR-181a strongly inhibited the effects of NET on MCF10A cells and abrogated NET-stimulated PGRMC1, EGFR, and mTOR expression. CONCLUSIONS: Activation of the PGRMC1/EGFR-PI3K/Akt/mTOR signaling pathway is the primary mechanism underlying the pro-tumorigenesis effects of NET on human breast epithelial MCF10A cells. Additionally, miR-181a can suppress the effects of NET on these cells. These data suggest a therapeutic potential for miR-181a in reducing or preventing the risk of breast cancer in hormone replacement therapy using NET.
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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) | 0.001 | 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) | 0.003 | 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