The induction of cyclooxygenase-1 by a tobacco carcinogen in U937 human macrophages is correlated to the activation of NF-κB
Bibliographic record
Abstract
The nicotine-derived 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), present in tobacco smoke, is most likely involved in lung carcinogenesis in smokers. We demonstrated previously that non-steroidal anti-inflammatory drugs (NSAIDs) inhibit NNK-induced lung tumorigenesis, although the mechanism(s) is unknown. The present study demonstrates that, in U937 human macrophages, cyclooxygenase (COX)-1 and -2 are involved in the bioactivation of NNK to electrophilic mutagenic intermediates. We observed that acetylsalicylic acid and NS-398 decrease COX-dependent NNK activation in U937 cells by 66 and 37%, respectively. NSAIDs also decrease prostaglandin E(2) (PGE(2)) synthesis, which is induced in a dose-dependent manner, reaching a 7-fold increase, in NNK-treated human U937 cells. We observed that NNK induces COX-1 expression and activates the nuclear factor-kappaB (NF-kappaB), in U937 cells. N:-acetyl-L-cysteine and pyrrolidinedithiocarbamate, two inhibitors of reactive oxygen species (ROS), inhibit NNK-induced PGE2 synthesis by 41 and 44%, respectively. These data suggest that ROS, generated during pulmonary metabolism of NNK could act as signal transduction messengers and activate NF-kappaB, which will subsequently induce COX-1 activity and increase PGE(2) synthesis. These results reveal a novel aspect of tobacco carcinogenesis, and give us insight into the mechanisms of chemoprevention by NSAIDs. Accordingly, inhibition of NF-kappaB activation, leading to the inhibition of COX, offers a new approach in lung cancer prevention.
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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) | 0.001 | 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".