Construction and evaluation of endometriosis diagnostic prediction model and immune infiltration based on efferocytosis-related genes.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38370978", "kind": "science-record", "revision": 5, "content_hash": "sha256:d87002d59feb6a9345b03905fe943472a26ed507a69a86aa25650ebd5b4f9b7d", "created_at": "2026-11-01T19:20:19Z", "updated_at": "2026-11-01T22:53:42Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction and evaluation of endometriosis diagnostic prediction model and immune infiltration based on efferocytosis-related genes.", "doi": "10.3389/fmolb.2023.1298457", "pmid": "38370978", "authors": ["Fang-Li Pei", "Jin-Jin Jia", "Shu-Hong Lin", "Xiao-Xin Chen", "Li-Zheng Wu", "Zeng-Xian Lin", "Bo-Wen Sun", "Cheng Zeng"], "journal": "Frontiers in molecular biosciences", "year": 2023, "abstract": "Background: Endometriosis (EM) is a long-lasting inflammatory disease that is difficult to treat and prevent. Existing research indicates the significance of immune infiltration in the progression of EM. Efferocytosis has an important immunomodulatory function. However, research on the identification and clinical significance of efferocytosis-related genes (EFRGs) in EM is sparse. Methods: The EFRDEGs (differentially expressed efferocytosis-related genes) linked to datasets associated with endometriosis were thoroughly examined utilizing the Gene Expression Omnibus (GEO) and GeneCards databases. The construction of the protein-protein interaction (PPI) and transcription factor (TF) regulatory network of EFRDEGs ensued. Subsequently, machine learning techniques including Univariate logistic regression, LASSO, and SVM classification were applied to filter and pinpoint diagnostic biomarkers. To establish and assess the diagnostic model, ROC analysis, multivariate regression analysis, nomogram, and calibration curve were employed. The CIBERSORT algorithm and single-cell RNA sequencing (scRNA-seq) were employed to explore immune cell infiltration, while the Comparative Toxicogenomics Database (CTD) was utilized for the identification of potential therapeutic drugs for endometriosis. Finally, immunohistochemistry (IHC) and reverse transcription quantitative polymerase chain reaction (RT-qPCR) were utilized to quantify the expression levels of biomarkers in clinical samples of endometriosis. Results: Our findings revealed 13 EFRDEGs associated with EM, and the LASSO and SVM regression model identified six hub genes (ARG2, GAS6, C3, PROS1, CLU, and FGL2). Among these, ARG2, GAS6, and C3 were confirmed as diagnostic biomarkers through multivariate logistic regression analysis. The ROC curve analysis of GSE37837 (AUC = 0.627) and GSE6374 (AUC = 0.635), along with calibration and DCA curve assessments, demonstrated that the nomogram built on these three biomarkers exhibited a commendable predictive capacity for the disease. Notably, the ratio of nine immune cell types exhibited significant differences between eutopic and ectopic endometrial samples, with scRNA-seq highlighting M0 Macrophages, Fibroblasts, and CD8 Tex cells as the cell populations undergoing the most substantial changes in the three biomarkers. Additionally, our study predicted seven potential medications for EM. Finally, the expression levels of the three biomarkers in clinical samples were validated through RT-qPCR and IHC, consistently aligning with the results obtained from the public database. Conclusion: we identified three biomarkers and constructed a diagnostic model for EM in this study, these findings provide valuable insights for subsequent mechanistic research and clinical applications in the field of endometriosis.", "claims": [{"id": "claim-method", "statement": "Following that, the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) regression models were employed to filter and determine diagnostic markers, establishing a predictive model for disease diagnosis (Gentleman et al., 2005).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38370978", "locator": "sections[10] \"2.4.2 Screening diagnostic biomarkers by machine learning models\" paragraph 2", "quote": "Following that, the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) regression models were employed to filter and determine diagnostic markers, establishing a predictive model for disease diagnosis (Gentleman et al., 2005)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38370978", "locator": "sections[31] \"Data availability statement\" paragraph 2", "quote": "The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation."}], "state": "complete"}, {"id": "claim-target-result", "statement": "ROC curves depicted the satisfactory capability of the three-gene prediction model, with an AUC value of 0.627 (95% CI: 0.437–0.816) in the GSE37837 dataset and 0.635 (95% CI: 0.449–0.821) in the GSE6374 dataset (Figure 7D).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "ROC curves depicted the satisfactory capability of the three-gene prediction model, with an AUC value of 0.627 (95% CI: 0.437–0.816) in the GSE37837 dataset and 0.635 (95% CI: 0.449–0.821) in the GSE6374 dataset (Figure 7D)."}, "evidence": [{"asset": "source:pmid:38370978", "locator": "sections[22] \"3.4 Establishment and evaluation of diagnostic prediction model\" paragraph 2", "quote": "ROC curves depicted the satisfactory capability of the three-gene prediction model, with an AUC value of 0.627 (95% CI: 0.437–0.816) in the GSE37837 dataset and 0.635 (95% CI: 0.449–0.821) in the GSE6374 dataset (Figure 7D)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "ROC curves depicted the satisfactory capability of the three-gene prediction model, with an AUC value of 0.627 (95% CI: 0.437–0.816) in the GSE37837 dataset and 0.635 (95% CI: 0.449–0.821) in the GSE6374 dataset (Figure 7D).", "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:38370978"], "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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The CIBERSORT algorithm and single-cell RNA sequencing (scRNA-seq) were employed to explore immune", "RT algorithm and single-cell RNA sequencing (scRNA-seq) were employed to explore immune cell infilt", "utopic and ectopic endometrial samples, with scRNA-seq highlighting M0 Macrophages, Fibroblasts, an", "e deliberated statistically significant. 3.6 Single-cell RNA sequencing analysis of immune infiltration in endometri"], "order_basis": "paper_text_order:1161", "id": "assay:1"}, {"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["yed. The CIBERSORT algorithm and single-cell RNA sequencing (scRNA-seq) were employed to explore immune", "d statistically significant. 3.6 Single-cell RNA sequencing analysis of immune infiltration in endometri", "expression level in macrophages. Single-cell RNA sequencing analysis (scRNA-seq) of immune infiltration:"], "order_basis": "paper_text_order:1173", "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": 0.99, "evidence": ["herapeutic drugs for endometriosis. Finally, immunohistochemistry (IHC) and reverse transcription quantitative", "ndometriosis. Finally, immunohistochemistry (IHC) and reverse transcription quantitative poly", "l samples were validated through RT-qPCR and IHC, consistently aligning with the results obta", "lized for the analysis and interpretation of IHC findings. 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