Extracellular Signal-Regulated Kinase Is a Target of Cyclooxygenase-1-Peroxisome Proliferator-Activated Receptor-δ Signaling in Epithelial Ovarian Cancer
Bibliographic record
Abstract
The underlying causes of epithelial ovarian cancer (EOC) are unclear, and treatment options for patients with advanced disease are limited. There is evidence that the use of nonsteroidal anti-inflammatory drugs is associated with decreased risk of developing EOC. Nonsteroidal anti-inflammatory drugs inhibit cyclooxygenase (COX)-1 and COX-2, which catalyze prostaglandin biosynthesis. We previously showed that mouse and human EOCs have increased levels of COX-1, but not COX-2, and a COX-1-selective inhibitor, SC-560, attenuates prostaglandin production and tumor growth. However, the downstream targets of COX-1 signaling in EOC are not yet known. To address this question, we evaluated peroxisome proliferator-activated receptor delta (PPARdelta) expression and function in EOC. We found that EOC cells express high levels of PPARdelta, and neutralizing PPARdelta function reduces tumor growth in vivo. More interestingly, aspirin, a nonsteroidal anti-inflammatory drug that preferentially inhibits COX-1, compromises PPARdelta function and cell growth by inhibiting extracellular signal-regulated kinases 1/2, members of the mitogen-activated protein kinase family. Our study, for the first time, shows that whereas PPARdelta can be a target of COX-1, extracellular signal-regulated kinase is a potential target of PPARdelta. The ability of aspirin to inhibit EOC growth in vivo is an exciting finding because of its low cost, lack of cardiovascular side effects, and availability.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".