Progesterone receptors induce FOXO1-dependent senescence in ovarian cancer cells
Bibliographic record
Abstract
Loss of nuclear progesterone receptors (PR) and low circulating progesterone levels are associated with increased ovarian cancer (OC) risk. However, PR are abundantly expressed in a significant percentage of serous and endometrioid ovarian tumors; patients with PR+ tumors typically experience longer progression-free survival relative to those with PR-null tumors. The molecular mechanisms of these protective effects are poorly understood. To study PR action in OC in the absence of added estrogen (i.e., needed to induce robust PR expression), we created ES-2 OC cells stably expressing vector control or GFP-tagged PR-B (GFP-PR). Progestin (R5020) stimulation of ES-2 cells stably expressing GFP-PR induced cellular senescence characterized by altered cellular morphology, prolonged survival, senescence-associated β-galactosidase activity, G1 cell cycle arrest and upregulation of the cell cycle inhibitor, p21, as well as the Forkhead-box transcription factor, FOXO1; these results repeated in unmodified ER+/PR+ PEO4 OC cells. PR-B and FOXO1 were detected within the same PRE-containing regions of the p21 upstream promoter. Knockdown of p21 resulted in molecular compensation via FOXO1-dependent upregulation of numerous FOXO1 target genes (p15, p16, p27) and an increased rate of senescence. Inhibition of FOXO1 (with AS1842856) or stable FOXO1 knockdown inhibited progestin-induced p21 expression and blocked progestin-induced senescence. Overall, these findings support a role for PR as a tumor suppressor in OC cells, which exhibits inhibitory effects by inducing FOXO1-dependent cellular senescence. Clinical "priming" of the PR-FOXO1-p21 signaling pathway using PR agonists may provide a useful strategy to induce irreversible cell cycle arrest and thereby sensitize OC cells to existing chemotherapies as part of combination "two-step" therapies.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".