Distinct Molecular Pathways Mediate Progesterone-Induced Growth Inhibition And Focal Adhesion
Bibliographic record
Abstract
We have reported previously that reactivation of progesterone receptor (PR) expression in estrogen receptor (ER)- and PR-negative MDA-MB-231 breast cancer cells enabled progesterone to inhibit cell growth and invasiveness, and to induce remarkable focal adhesions. The present study addressed molecular mechanisms that mediate these anticancer effects of progesterone in the PR-transfected breast cancer cells ABC28. In response to progesterone treatment are the marked up-regulation of cyclin-dependent kinase inhibitor protein p21WAF1/CIP1 and decreased expression of cyclin A, cyclin B1, and cyclin D1 that are required for G1 progression and during cell mitosis. Progesterone also induced down-regulation of phosphorylated MAPK (p42/44 MAPK). Furthermore, this study also demonstrated that MEK inhibitor PD98059 that inhibits the phosphorylation of p42/44 MAPK also caused reduction of cyclin D1 level and inhibition of cell proliferation. These results suggest that inhibition of p42/44 MAPK pathway is part of the mechanisms mediating progesterone's growth-inhibitory effect. On the other hand, progesterone-induced focal adhesion is mediated by separate pathway. Whereas PD98059 exhibited no effects on cell adhesion, inhibitory antibody to beta1-integrin was able to reverse progesterone-induced focal adhesion and progesterone-induced increase in the phosphorylation of focal adhesion kinase. On the other hand, beta1-integrin antibody had no effect on progesterone-mediated growth inhibition and on progesterone-mediated expression of cyclins p21CIP1/WAF1 and phosphorylation of P42/P44 MAPK. In the context of complex functions of progesterone in breast cancer and reproductive organs, identification of distinct pathways offers new strategies for designing therapeutic agents to target the specific pathway so as to minimize the side effects.
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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) | 0.001 | 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) | 0.002 | 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 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".