Anoikis-Related Genes Can Accurately Predict the Occurrence of Endometriosis: A Retrospective Cohort Study via Machine Learning Analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40465087", "kind": "science-record", "revision": 4, "content_hash": "sha256:ca99268dbe7fbbe358c1393fef3f8129b697c8b9a90ca043341732ffd33a4b7f", "created_at": "2026-11-01T20:30:35Z", "updated_at": "2026-11-01T22:56:37Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Anoikis-Related Genes Can Accurately Predict the Occurrence of Endometriosis: A Retrospective Cohort Study via Machine Learning Analysis.", "doi": "10.1007/s10528-025-11151-x", "pmid": "40465087", "authors": ["Lin Hong", "Lan Zheng", "Yu-Feng He", "Ya-Xing Fang", "Hui Chen", "Kang-Jia Chen", "Shu-Guang Zhou"], "journal": "Biochemical genetics", "year": 2025, "abstract": "Endometriosis is one of the most common benign gynecological disorders, characterized by persistent pain and challenges with fertility. Anoikis, a specific form of apoptosis, occurs when cells detach from the extracellular matrix. Recent researches have revealed that anoikis resistance is the most important prerequisite for endometriosis development, but it's still not entirely apparent, yet, what part anoikis plays in endometriosis pathogenesis. We obtained the GSE141549 dataset from the GEO database, which served as our training cohort. Anoikis-related genes (ANRGs), sourced from the GeneCards database, were integrated with differentially expressed genes (DEGs) identified in GSE141549. Consequently, we obtained a collection of differentially expressed anoikis-related genes (DE-ANRGs). Subsequently, we employed machine learning algorithms to screen for key diagnostic DE-ANRGs and developed a nomogram model to predict and diagnose endometriosis. Ultimately, we validated our findings through in vitro experiments and an online endometriosis database. Within the training cohort, a total of 47 DE-ANRGs were identified. Furthermore, three machine learning methods pinpointed four diagnostic genes (CAV1, PDK4, CSPG4, SERPINE1). Based on these genes, we constructed a nomogram to facilitate the prediction and clinical diagnosis of endometriosis. To assess model's predictive accuracy, clinical adaptability, and discriminative ability. We performed calibration curves, Receiver Operating Characteristic curves and decision curve analysis. All assessments demonstrated our model's outstanding performance. Ultimately, consistent expression trends of four genes were observed in GSE7305 test cohort, clinical specimens, and the Turku database when compared to the training cohort. In addition, we also have revealed the immune landscape differences, which may offer new promising immunotherapeutic targets for endometriosis patients in the future. In addition to offering novel perspectives on the role of anoikis in endometriosis pathogenesis, our analysis also identified a panel of distinctive biomarkers with significant diagnostic potential.", "claims": [{"id": "claim-method", "statement": "We utilized three different machine learning approaches: LASSO, RF, and SVM-RFE.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40465087", "locator": "sections[18] \"Identifying Diagnostic Genes by Machine Learning Approaches\" paragraph 2", "quote": "We utilized three different machine learning approaches: LASSO, RF, and SVM-RFE."}], "state": "complete"}, {"id": "claim-data-access", "statement": "No datasets were generated or analysed during the current study.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40465087", "locator": "sections[29] \"Data Availability\" paragraph 2", "quote": "No datasets were generated or analysed during the current study."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Three machine-learning methods identified four diagnostic genes—CAV1, PDK4, CSPG4, and SERPINE1—using GSE141549 from the GEO database as the training cohort.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40465087", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "We obtained the GSE141549 dataset from the GEO database, which served as our training cohort."}, {"asset": "source:pmid:40465087", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Furthermore, three machine learning methods pinpointed four diagnostic genes (CAV1, PDK4, CSPG4, SERPINE1)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Three machine-learning methods identified four diagnostic genes—CAV1, PDK4, CSPG4, and SERPINE1—using GSE141549 from the GEO database as the training cohort.", "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:40465087"], "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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