Progesterone Receptor Inhibits Proliferation of Human Breast Cancer Cells via Induction of MAPK Phosphatase 1 (MKP-1/DUSP1)
Bibliographic record
Abstract
The roles of progesterone (P(4)) and of progesterone receptor (PR) in development and pathogenesis of breast cancer remain unclear. In this study, we observed that treatment of T47D breast cancer cells with progestin antagonized effects of fetal bovine serum (FBS) to stimulate cell proliferation, whereas siRNA-mediated knockdown of endogenous PR abrogated progestin-mediated anti-proliferative effects. To begin to define mechanisms for the anti-proliferative action of P(4)/PR, we considered the role of MAPK phosphatase 1 (MKP-1/DUSP1), which catalyzes dephosphorylation and inactivation of MAPKs. Progestin treatment of T47D cells rapidly induced MKP-1 expression in a PR-dependent manner. Importantly, P(4) induction of MKP-1 was associated with reduced levels of phosphorylated ERK1/2, whereas siRNA knockdown of MKP-1 blocked progestin-mediated ERK1/2 dephosphorylation and repression of FBS-induced cell proliferation. The importance of PR in MKP-1 expression was supported by findings that MKP-1 and PR mRNA levels were significantly correlated in 30 human breast cancer cell lines. By contrast, no correlation was observed with the glucocorticoid receptor, a known regulator of MKP-1 in other cell types. ChIP and luciferase reporter assay findings suggest that PR acts in a ligand-dependent manner through binding to two progesterone response elements downstream of the MKP-1 transcription start site to up-regulate MKP-1 promoter activity. PR also interacts with two Sp1 sites just downstream of the transcription start site to increase MKP-1 expression. Collectively, these findings suggest that MKP-1 is a critical mediator of anti-proliferative and anti-inflammatory actions of PR in the breast.
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.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".