Telomere-related genes as potential biomarkers to predict endometriosis and immune response: Development of a machine learning-based risk model.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36968845", "kind": "science-record", "revision": 3, "content_hash": "sha256:a522f7a1d1f7102f64fd5c65170a2fdb40f6f68507f8c0b840ab086c2e04305c", "created_at": "2026-11-01T19:01:36Z", "updated_at": "2026-11-01T22:09:07Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Telomere-related genes as potential biomarkers to predict endometriosis and immune response: Development of a machine learning-based risk model.", "doi": "10.3389/fmed.2023.1132676", "pmid": "36968845", "authors": ["He Zhang", "Weimin Kong", "Yunkai Xie", "Xiaoling Zhao", "Dan Luo", "Shuning Chen", "Zhendong Pan"], "journal": "Frontiers in medicine", "year": 2023, "abstract": "Introduction: Endometriosis (EM) is an aggressive, pleomorphic, and common gynecological disease. Its clinical presentation includes abnormal menstruation, dysmenorrhea, and infertility, which seriously affect the patient's quality of life. However, the pathogenesis underlying EM and associated regulatory genes are unknown.\nMethods: Telomere-related genes (TRGs) were uploaded from TelNet. RNA-sequencing (RNA-seq) data of EM patients were obtained from three datasets (GSE5108, GSE23339, and GSE25628) in the GEO database, and a random forest approach was used to identify telomere signature genes and build nomogram prediction models. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis were used to identify the pathways involved in the action of the signature genes. Finally, the CAMP database was used to screen drugs for potential use in EM treatment.\nResults: Fifteen total genes were screened as EM-telomere differentially expressed genes. Further screening by machine learning obtained six genes as characteristic predictive of EM. Immuno-infiltration analysis of the telomeric genes showed that expressions including macrophages and natural killer cells were significantly higher in cluster A. Further enrichment analysis showed that the differential genes were mainly enriched in biological pathways like cell cycle and extracellular matrix. Finally, the Connective Map database was used to screen 11 potential drugs for EM treatment.\nDiscussion: TRGs play a crucial role in EM development, and are associated with immune infiltration and act on multiple pathways, including the cell cycle. Telomere signature genes can be valuable predictive markers for EM.", "claims": [{"id": "claim-method", "statement": "RNA-sequencing (RNA-seq) data of EM patients were obtained from three datasets (GSE5108, GSE23339, and GSE25628) in the GEO database, and a random forest approach was used to identify telomere signature genes and build nomogram prediction models.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36968845", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "RNA-sequencing (RNA-seq) data of EM patients were obtained from three datasets (GSE5108, GSE23339, and GSE25628) in the GEO database, and a random forest approach was used to identify telomere signature genes and build nomogram prediction models."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36968845", "locator": "sections[23] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Further screening by machine learning obtained six genes as characteristic predictive of EM.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36968845", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Further screening by machine learning obtained six genes as characteristic predictive of EM."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Further screening by machine learning obtained six genes as characteristic predictive of EM.", "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:36968845"], "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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RNA-sequencing (RNA-seq) data of EM patients were obtained", ") were uploaded from TelNet. RNA-sequencing (RNA-seq) data of EM patients were obtained from thre"], "order_basis": "paper_text_order:492", "id": "assay:1"}, {"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": ["Ethical Number: AEEI-2021-219). Five C57BL/6 mice were housed in a well-controlled, pathogen-free e", "icant between-mouse growth rate differences. Mice were euthanized 4 weeks later and both EM and ute", "s 2.5. Establishment of C57BL/6 experimental mouse endometriosis model C57BL/6 mice (specific p", "ted light/dark cycle (12/12 h, 23–25°C). One mouse was used as the endometrial tissue donor aft"], "order_basis": "paper_text_order:1877", "id": "assay:2"}, {"role": "data_generation", "definition_id": "ihc", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["th EM and uterine tissues were collected for immunohistochemical analysis. 3.6. Functional enrichment analysi"], "order_basis": "paper_text_order:2896", "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": ["RNA-sequencing (RNA-seq) data of EM patients were obtained from three datasets (GSE5108, GSE23339, and GSE25628) in the GEO database, and a random forest approach was used to identify telomere signature genes and build nomogram prediction models."], "order_basis": "consumes_earlier_output", "id": "assay:4"}]}