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

Fang-Li Pei, Jinjin Jia, Jin-Jin Jia, Shu-Hong Lin, Shu‐Hong Lin, Xiaoxin Chen, Xiao-Xin Chen, Li-Zheng Wu, Lizheng Wu, Zeng-Xian Lin, Bowen Sun, Bo-Wen Sun, Cheng Zeng
Frontiers in molecular biosciences · 2024 · vol. 10 , pp. 1298457 · doi:10.3389/fmolb.2023.1298457 · PMID:38370978 · W4390585204
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⚙ AI-generated summary by claude@2026-06, 2026-06-08 ⓘ

This study identified three efferocytosis-related genes (ARG2, GAS6, C3) as diagnostic biomarkers for endometriosis and developed a predictive model, also revealing altered immune cell infiltration and potential therapeutic drugs.

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⚙ AI-generated deep summary by claude@2026-06, 2026-06-10 ⓘ

This study used GEO microarray datasets of paired eutopic and ectopic endometrial samples to identify efferocytosis-related genes by integrating efferocytosis gene lists from GeneCards and KEGG, then intersecting these with endometriosis differentially expressed genes. Using PPI/TF network analyses and machine learning (univariate logistic regression, LASSO, and SVM), the authors narrowed to six hub efferocytosis-linked genes and confirmed three diagnostic biomarkers (ARG2, GAS6, and C3) by multivariate logistic regression, with a nomogram showing moderate diagnostic performance across testing cohorts (reported AUCs around 0.63) plus calibration/DCA evaluation. They also assessed immune infiltration differences between eutopic and ectopic tissues using CIBERSORT and scRNA-seq, reporting substantial changes in immune-related cell populations and predicting seven potential therapeutic drugs via CTD, with expression validation in clinical samples using RT-qPCR and immunohistochemistry. A key limitation explicitly reflected in the analysis design is that the diagnostic model’s performance and drug predictions are derived from bioinformatics reanalysis of public datasets and in silico methods rather than prospective clinical validation. This paper is centrally about endometriosis — it constructs and evaluates an endometriosis diagnostic prediction model based on efferocytosis-related gene expression and links these biomarkers to immune infiltration patterns.

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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.

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endometriosis

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