N-Acetyl Cysteine as a promising therapeutic approach in ovarian cancer: potential and perspectives
Bibliographic record
Abstract
Ovarian cancer is the seventh most common cancer in women and the eighth most common cause of cancer death worldwide. It is an aggressive disease with five-year survival rates below 45% and many patients relapse within 2 years. Further treatments become more intense, resulting in chemotherapy drug resistance and increased toxicity. This has created the need to develop new therapeutic strategies to improve the quality of life and treatment options for ovarian cancer patients. Studies have reported the role of cysteine in ovarian cancer, primarily as a precursor of glutathione (GSH), contributing to the endogenous antioxidant mechanism. The membrane-permeable cysteine precursor N-acetylcysteine (NAC) can enhance the intracellular cysteine pool and thus results in decreased oxidative stress. This characteristic provides NAC with a rationale as a potentially effective chemo-protectant in ovarian cancer treatment. In this review, we summarize the effects of NAC supplementation in ovarian cancer from recent preclinical studies. The role of NAC in chemotherapy response, and mechanisms to overcome chemo resistance in ovarian cancer (including targeting the Mirk/dyrk1B kinase pathway) are also explored. While NAC holds therapeutic promise in alleviating treatment-associated toxicities, its application in ovarian cancer requires careful consideration based on tumour subtype, redox context, and treatment timing. Future research incorporating subtype-specific models and clinical trials will be essential to delineate the precise role of NAC and optimize its integration into ovarian cancer treatment regimens.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".