Cross-talk between oxidative stress and lipid metabolism regulators reveals molecular clusters and immunological characterization in polycystic ovarian syndrome.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39143634", "kind": "science-record", "revision": 3, "content_hash": "sha256:87ff80f0d0e77f9707bdd3be7dd12c21588765593f6993ddbe89fb0c96385655", "created_at": "2026-11-01T20:29:14Z", "updated_at": "2026-11-01T22:12:28Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Cross-talk between oxidative stress and lipid metabolism regulators reveals molecular clusters and immunological characterization in polycystic ovarian syndrome.", "doi": "10.1186/s12944-024-02237-3", "pmid": "39143634", "authors": ["Cuiyu Tan", "Shuqiang Huang", "Liying Xu", "Tongtong Zhang", "Xiaojun Yuan", "Zhihong Li", "Miaoqi Chen", "Cairong Chen", "Qiuxia Yan"], "journal": "Lipids in health and disease", "year": 2024, "abstract": "BACKGROUND: Changes in the oxidative stress and lipid metabolism (OSLM) pathways play important roles in polycystic ovarian syndrome (PCOS) pathogenesis and development. Consequently, a systematic analysis of genes related to OSLM was conducted to identify molecular clusters and explore new biomarkers that are helpful for the diagnostic of PCOS.\nMETHODS: Gene expression and clinical data from 22 PCOS women and 14 normal women were obtained from the GEO database (GSE34526, GSE95728, and GSE106724). Consensus clustering identified OSLM-related molecular clusters, and WGCNA revealed co-expression patterns. The immune microenvironment was quantitatively assessed utilizing the CIBERSORT algorithm. Multiple machine learning models and connectivity map analyses were subsequently applied to explore potential biomarkers for PCOS, and nomograms were employed to develop a predictive multigene model of PCOS. Finally, the OSLM status of PCOS and the hub genes expression profiles were preliminarily verified using TUNEL, qRT‒PCR, western blot, and IHC assays in a PCOS mouse model.\nRESULTS: 19 differential expression genes (DEGs) related to OSLM were identified. Based on 19 DEGs that were strongly influenced by OSLM, PCOS patients were stratified into two distinct clusters, designated Cluster 1 and Cluster 2. Distinct differences in the immune cell proportions existed in normal and two PCOS clusters. The random forest showed the best results, with the least cross-entropy and the utmost AUC (cross-entropy: 0.111 AUC: 0.960). Among the 19 OSLM-related genes, CXCR1, ACP5, CEACAM3, S1PR4, and TCF7 were identified by a Bayesian network and had a good fit with PCOS disease risk by the nomogram (AUC: 0.990 CI: 0.968-1.000). TUNEL assays revealed more severe DNA damage within the ovarian granule cells of PCOS mice than in those of normal mice (P < 0.001). The RNA and protein expression levels of the five hub genes were significantly elevated in PCOS mice, which was consistent with the results of the bioinformatics analyses.\nCONCLUSION: A novel predictive model was constructed for PCOS patients and five hub genes were identified as potential biomarkers to offer novel insights into clinical diagnostic strategies for PCOS.", "claims": [{"id": "claim-method", "statement": "RF had the best adaptation (with the least cross-entropy and utmost AUC value) in twofold cross-validation (Fig. 5B-C).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39143634", "locator": "sections[30] \"Construction and exploration of machine learning algorithms to screen genes\" paragraph 2", "quote": "RF had the best adaptation (with the least cross-entropy and utmost AUC value) in twofold cross-validation (Fig. 5B-C)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The authors confirm that the data supporting the findings of this study are available within the article and its materials or from the corresponding authors upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39143634", "locator": "sections[47] \"notes\" paragraph 1", "quote": "The authors confirm that the data supporting the findings of this study are available within the article and its materials or from the corresponding authors upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The random forest showed the best results, with the least cross-entropy and the utmost AUC (cross-entropy: 0.111 AUC: 0.960).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39143634", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The random forest showed the best results, with the least cross-entropy and the utmost AUC (cross-entropy: 0.111 AUC: 0.960)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The random forest showed the best results, with the least cross-entropy and the utmost AUC (cross-entropy: 0.111 AUC: 0.960).", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:39143634"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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RESUL", "Compared with the normal group, qRT‒PCR and western blot analyses indicated that expression of the fi", "‒B). IHC results were similar to qRT‒PCR and western blot. Notably, ACP5 and S1PR4 were predominantly"], "order_basis": "paper_text_order:1148", "id": "assay:1"}, {"role": "data_generation", "definition_id": "ihc", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["fied using TUNEL, qRT‒PCR, western blot, and IHC assays in a PCOS mouse model. RESULTS: 19 di", "R1) was substantially elevated (Fig. 10A‒B). IHC results were similar to qRT‒PCR and western", "lar cells. C-D Negative control results. E-N IHC of five hub genes in mouse ovary tissue betw"], "order_basis": "paper_text_order:1166", "id": "assay:2"}, {"role": "data_generation", "definition_id": "animal_model", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["‒PCR, western blot, and IHC assays in a PCOS mouse model. RESULTS: 19 differential expression genes (", "‒PCR, western blot, and IHC assays in a PCOS mouse model. RESULTS: 19 differential expression g", "and evaluation Based on a high-fat diet, the mouse model of PCOS was created using DHEA. The body wei", "up (Fig. 8A). The testosterone in PCOS group mice were markedly elevated, similar to those in PCOS"], "order_basis": "paper_text_order:1187", "id": "assay:3"}, {"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["RF had the best adaptation (with the least cross-entropy and utmost AUC value) in twofold cross-validation (Fig. 5B-C)."], "order_basis": "consumes_earlier_output", "id": "assay:4"}]}