ArticleJournal of ovarian research2025
Gene association study between polycystic ovary syndrome and metabolic syndrome: a transcriptomic analysis and machine learning approach.
Article in Journal of ovarian research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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.
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Who cites it
2 citing papers in PubMed.
- Unraveling the mechanisms of PCOS: the interplay between gut microbiota and the immune system.Journal of ovarian research · 2026Review
- Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundPatients with polycystic ovary syndrome (PCOS) often experience a range of metabolic comorbidities, suggesting a potential association between PCOS and metabolic syndrome (MetS). However, this potential link has not yet been fully elucidated.
methodsThis study employed transcriptomic analysis and machine learning techniques to identify key genes and signaling pathways associated with both PCOS and MetS. Differentially expressed genes (DEGs) were analyzed, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Machine learning algorithms were used to identify hub genes, and their diagnostic potential was assessed using Receiver Operating Characteristic (ROC) curves.
resultsA total of 373 DEGs were identified in PCOS, and 516 DEGs in MetS, with 14 overlapping genes considered critical for both conditions. Six hub genes including Dihydropyrimidinase-like 4(DPYSL4), FBJ osteosarcoma oncogene(FOS), Jun dimerization protein 2(JDP2), Stearoyl-CoA desaturase(SCD), Tribbles pseudokinase 1(TRIB1), Zinc finger protein 331(ZNF331) were selected through various machine learning methods. Enrichment analyses revealed that these genes significantly influence apoptosis, TNF signaling, and lipid metabolism pathways, highlighting their roles in the pathogenesis of PCOS and MetS.
conclusionsThese findings suggest that these genes may serve as potential therapeutic targets for the prevention and treatment of comorbidities in patients with PCOS and MetS. The identified hub genes play significant roles in the development of PCOS and MetS, underscoring the need for further research on these genes. This study offers insights into molecular interactions and potential biomarkers for early diagnosis and therapeutic targets for these syndromes. Future studies should aim to validate these findings in larger cohorts to enhance their clinical applicability. CLINICAL TRIAL NUMBER: Not applicable.
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