Cyclooxygenase-1 Is a Potential Target for Prevention and Treatment of Ovarian Epithelial Cancer
Bibliographic record
Abstract
The precise genetic and molecular defects underlying epithelial ovarian cancer (EOC) remain largely unknown, and treatment options for patients with advanced disease are limited. Cyclooxygenases (COX-1 and COX-2) catalyze the conversion of arachidonic acid to prostaglandins. Whereas overwhelming evidence suggests a role for COX-2 in a variety of cancers, the contribution of COX-1 remains much less explored. The expression status of COX isoforms in ovarian cancers also remains confusing. We have previously shown that human epithelial ovarian tumors have increased levels of COX-1 but not COX-2. To more carefully examine the role of COXs in ovarian cancer, we used a mouse model of EOC in which genetic and oncogenic modifications were experimentally engineered into ovarian surface epithelial cells (OSE) thought to be the cells of origin for human EOC. These OSE cells produce tumors when allografted into host mice. Using multiple approaches, we observed that OSE cells and the tumors comprised of these cells express high levels of COX-1 but not COX-2. Prostacyclin (PGI(2)) is the major prostaglandin generated downstream of COX-1 in these cells, and SC-560, a COX-1-selective inhibitor, dramatically inhibits PGI(2) production. More importantly, SC-560 reduced the growth of tumors when OSE cells were allografted in nude female mice. In contrast, the COX-2-selective inhibitor celecoxib had little effect on tumor growth. The growth inhibitory effects of SC-560 result from reduced cell proliferation and/or accelerated apoptosis. Our results imply COX-1 as a target for the prevention and/or treatment of EOC.
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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) | 0.004 | 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".