NF-kappaB, macrophage migration inhibitory factor and cyclooxygenase-inhibitions as likely mechanisms behind the acetaminophen- and NSAID-prevention of the ovarian cancer.
Bibliographic record
Abstract
Recent epidemiological studies indicated risk reductions in ovarian cancer with consumption of acetaminophen or non-steroid anti-inflammatory drugs. Until now, there is not a systematic analysis, why these agents may reduce risk of ovarian cancer, as it has been performed to explain aspirin-reduction of colon cancer risk. This review tries to explain molecular mechanisms pertinent to acetaminophen- and NSAID-reduction of ovarian cancer. It is proposed that the major mechanism by these anti-inflammatory agents is a shared pathway dependent on the suppression of NF-kappaB activity, which may subsequently decrease transcription of growth factors, chemokines and proteases such as COX-2, VEGF, IL-8/CXCL8, MCP-1/CCL-2, MIP1alpha/CCL-3, tPA and uPA, which are shown to be elevated in ovarian carcinoma, and which play diverse roles such as inducing angiogenesis, invasion, autocrine growth loops and resistance to apoptosis. Besides these, specific mechanisms of action can be attributed to acetaminophen-reduction of ovarian cancer risk via I. Induction of specific reproductive atrophy due its sex-steroid resembling phenolic ring; II. Reduction of glutathione pools due to its NAPQI metabolite, which may play an important role for sterilizing pre-malignant ovarian lesions, since they are shown to lack proper levels of glutathione; III. Inhibition of tautomerization activity of MIF (macrophage migration inhibitory factor), which is shown to be released from ovarian cancer, and which is necessary for proper ovulation; IV. Inhibition of cytokine-induced and endothelia-origined cyclooxygenases. Except the chemosensitization studies, acetaminophen and NSAIDs should be investigated in animal models to test likely benefits in ovarian cancer, since most of their activity may origin from intervening with the cancer growth-stimulating inflammatory stimuli, rather than with the direct cellular toxicity.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".