A Systematic Review of the Molecular Mechanisms Involved in the Association Between <scp>PCOS</scp> and Endometrial and Ovarian Cancers
Bibliographic record
Abstract
Polycystic ovary syndrome (PCOS), a major cause of female infertility, affects 4%-20% of reproductive-age women. Metabolic and hormonal alterations are key features of PCOS, potentially raising the risk of endometrial (EC) and ovarian (OVCA) cancers. This systematic review aims to summarise the proposed molecular mechanisms involved in the association between PCOS and EC or OVCA. This is achieved by conducting a thorough literature review and utilising specific search terms to identify all relevant studies published in English from 2010 to December 2022. PRISMA was followed, and the protocol was registered on PROSPERO (CRD42022375461). The QUADAS-2 tool and Review Manager Software were employed to evaluate study quality and risk of bias respectively. Forty-five eligible studies were selected with molecular signatures based on genomic, transcriptomic, metabolomic, proteomic and epigenetic analyses. Genes and their products deregulated in EC and/or OVCA were identified, including BRCA1, MLH1, NQO1 and ESR1, which were also deregulated in PCOS. Serum levels of IGF1, IGFBP1, SREBP1 and visfatin in women with PCOS were also identified as potential biomarkers of enhanced EC risk. Salusin-β serum levels in individuals with PCOS were identified as a potential biomarker for increased risk of OVCA. Gene signature-based drug repositioning identified several drug candidates: metformin, fenofibrate, fatostatin, melatonin, resveratrol and quercetin, some already established and prescribed for PCOS. In conclusion, this study provides a strong basis for further research to confirm the identified molecular signatures and associated causal links for potential therapeutic prevention strategies for EC and OVCA in women with PCOS.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".