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<p>Endometriosis is a chronic inflammatory disease characterized by ectopic endometrial tissue growth, causing pain and infertility. Diagnosis is often delayed due to reliance on imaging and invasive methods, highlighting the need for non-invasive biomarkers for early detection. We have performed a case–control study with 78 endometriosis patients and 48 healthy controls undergoing salpingectomy. Blood samples were analyzed for soluble cytokines and immune checkpoints using a bead-based flow cytometry assay. Logistic regression models combining immunological markers with demographic and clinical variables were developed to assess diagnostic performance using receiver operating characteristic and area under the curve (AUC). Endometriosis patients showed distinct circulating immune profiles with altered cytokine and immune checkpoint levels. A predictive model based on eight immunological markers demonstrated high diagnostic performance with an AUC of 0.8881, 72.92% specificity and 92.31% sensitivity. A minimal immunological signature incorporating four soluble factors (sCD25, sPD-L1, sLAG-3, and IP-10) alongside demographic variables (age and BMI) maintained a robust diagnostic efficacy with an AUC of 0.8243, 60.42% specificity and 92.31% sensitivity. Both models’ diagnostic capacity was consistent regardless of patient demographics or clinical features. Our findings support the potential of a liquid biopsy approach based on immunological signatures for the efficient and non-invasive diagnosis of endometriosis.</p>

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Identification of a circulating immunological signature as a liquid biopsy approach for the diagnosis of endometriosis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月23日

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  • Alicia Hernández,
  • Olivia Fernández-Medina,
  • Paula Almellones Araiz,
  • Verónica Terrón,
  • Tamara Ruiz Martínez,
  • Ana López Carrasco,
  • Maria Luisa Juan Rodrigo,
  • Eduardo López-Collazo,
  • Carlos Del Fresno,
  • Emanuela Spagnolo

摘要

Endometriosis is a chronic inflammatory disease characterized by ectopic endometrial tissue growth, causing pain and infertility. Diagnosis is often delayed due to reliance on imaging and invasive methods, highlighting the need for non-invasive biomarkers for early detection. We have performed a case–control study with 78 endometriosis patients and 48 healthy controls undergoing salpingectomy. Blood samples were analyzed for soluble cytokines and immune checkpoints using a bead-based flow cytometry assay. Logistic regression models combining immunological markers with demographic and clinical variables were developed to assess diagnostic performance using receiver operating characteristic and area under the curve (AUC). Endometriosis patients showed distinct circulating immune profiles with altered cytokine and immune checkpoint levels. A predictive model based on eight immunological markers demonstrated high diagnostic performance with an AUC of 0.8881, 72.92% specificity and 92.31% sensitivity. A minimal immunological signature incorporating four soluble factors (sCD25, sPD-L1, sLAG-3, and IP-10) alongside demographic variables (age and BMI) maintained a robust diagnostic efficacy with an AUC of 0.8243, 60.42% specificity and 92.31% sensitivity. Both models’ diagnostic capacity was consistent regardless of patient demographics or clinical features. Our findings support the potential of a liquid biopsy approach based on immunological signatures for the efficient and non-invasive diagnosis of endometriosis.