Evidence map›Paper›PMID 40642398›Full record

ArticleAcademia oncology2025

N-Acetyl Cysteine as a promising therapeutic approach in ovarian cancer: potential and perspectives.

Erin A Kindlon, Graham P Pidgeon

Abstract read
In one paragraph

Article in Academia oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Erin A KindlonTranslational Oncology, Trinity Translational Medicine Institute, Trinity College Dublin, Dublin D02PN40, Ireland.
Graham P PidgeonTranslational Oncology, Trinity Translational Medicine Institute, Trinity College Dublin, Dublin D02PN40, Ireland.

Funding

Role of Thromboxane in Prostate Cancer ProgressionR01CA114051 · NCI · WAYNE STATE UNIVERSITY · PI HONN, KENNETH V · 2006 to 2010
$1.6M
NCI NIH HHS R01 CA114051
6 · The paper itself

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.

Indexed as

chemotherapyclinical trialsN-Acetyl Cysteineovarian cancertoxicity

Identifiers

PMID40642398
PMCPMC12245165

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.