Cisplatin alters nitric oxide synthase levels in human ovarian cancer cells: involvement in p53 regulation and cisplatin resistance
Bibliographic record
Abstract
The present study determines if (1) basal protein levels of nitric oxide (NO) synthases (eNOS, iNOS, and nNOS) are different in cisplatin-sensitive (OV2008) and counterpart cisplatin-resistant (C13(*)) human ovarian cancer cells, (2) cisplatin alters NOS levels, (3) NO donor causes apoptosis and p53 upregulation, (4) NO donor sensitizes C13(*) cells to cisplatin via p53 upregulation (determined by p53 siRNA gene-knockdown), and (5) inhibition of endogenous NOS alters cisplatin-induced apoptosis. Basal iNOS levels were higher in OV2008 cells than in C13(*) cells. Cisplatin upregulated iNOS, but dramatically reduced eNOS and nNOS, in OV2008 cells only. Failure of cisplatin to upregulate iNOS and downregulate eNOS/nNOS in cisplatin-resistant C13(*) cells may be an aetiological factor in the development of resistance. The NO donor S-nitroso-N-acetylpenicillamine (SNAP) increased p53 protein levels and induced apoptosis in both cell types, and enhanced cisplatin-induced apoptosis in C13(*) cells in a p53-dependent manner (i.e., enhancement blocked by p53 siRNA). Specific iNOS inhibitor 1400W partially blocked cisplatin-induced apoptosis in OV2008 cells. In cisplatin-resistant C13(*) cells, blocking all NOSs with N(G)-amino-L-arginine dramatically changed these cells from cisplatin-resistant to cisplatin-sensitive, greatly potentiating cisplatin-induced apoptosis. The data suggest important roles for the three NOSs in regulating chemoresistance to cisplatin in ovarian cancer cells.
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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 | 0.000 | 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) | 0.000 | 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".