Regulation of Prostaglandin Metabolism by Calcitriol Attenuates Growth Stimulation in Prostate Cancer Cells
Bibliographic record
Abstract
Calcitriol exhibits antiproliferative and pro-differentiation effects in prostate cancer. Our goal is to further define the mechanisms underlying these actions. We studied established human prostate cancer cell lines and primary prostatic epithelial cells and showed that calcitriol regulated the expression of genes involved in the metabolism of prostaglandins (PGs), known stimulators of prostate cell growth. Calcitriol significantly repressed the mRNA and protein expression of prostaglandin endoperoxide synthase/cyclooxygenase-2 (COX-2), the key PG synthesis enzyme. Calcitriol also up-regulated the expression of 15-hydroxyprostaglandin dehydrogenase, the enzyme initiating PG catabolism. This dual action was associated with decreased prostaglandin E2 secretion into the conditioned media of prostate cancer cells exposed to calcitriol. Calcitriol also repressed the mRNA expression of the PG receptors EP2 and FP, providing a potential additional mechanism of suppression of the biological activity of PGs. Calcitriol treatment attenuated PG-mediated functional responses, including the stimulation of prostate cancer cell growth. The combination of calcitriol with nonsteroidal anti-inflammatory drugs (NSAIDs) synergistically acted to achieve significant prostate cancer cell growth inhibition at approximately 2 to 10 times lower concentrations of the drugs than when used alone. In conclusion, the regulation of PG metabolism and biological actions constitutes a novel pathway of calcitriol action that may contribute to its antiproliferative effects in prostate cells. We propose that a combination of calcitriol and nonselective NSAIDs might be a useful chemopreventive and/or therapeutic strategy in men with prostate cancer, as it would allow the use of lower concentrations of both drugs, thereby reducing their toxic 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) | 0.000 | 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) | 0.001 | 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".