MicroRNA-181a suppresses norethisterone-promoted tumorigenesis of breast epithelial MCF10A cells through the PGRMC1/EGFR–PI3K/Akt/mTOR signaling pathway
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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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".