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<p>Endometriosis is a chronic inflammatory condition marked by the presence of endometrial-like tissue outside the uterus, often co-occurring with leiomyoma and presenting a diagnostic challenge. We analyzed 96 plasma cytokines and inflammatory markers in 86 women undergoing surgery for suspected endometriosis, using multiplex assays and unsupervised clustering methods. Patients were classified using both rASRM and the more granular #Enzian system to assess lesion-specific and stage-specific biomarker patterns. We identified five distinct patient clusters reflecting disease heterogeneity, with improved resolution using the #Enzian classification. Notably, the presence of leiomyoma influenced cytokine profiles, potentially obscuring biomarker signals. Key biomarkers including perforin, TRAIL, sFasL, IL-17F, PDGF, VEGFA, and MCP-2 were associated with disease presence and stage. These findings highlight the value of advanced classification systems and emphasize the importance of accounting for comorbid conditions. Our results support the development of non-invasive biomarker panels for earlier and more accurate diagnosis of endometriosis.</p>

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Enhanced analysis of endometriosis patients’ plasma using #Enzian annotation highlights potential biomarkers for early-stages of disease

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月28日

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  • Daniel Rodriguez Gutierrez,
  • Alina Astourian,
  • Marianne Spalinger,
  • Lucie Berclaz,
  • Monique Hartmann,
  • Julian Metzler,
  • Isabell Witzel,
  • Patrick Imesch,
  • Valentina Vongrad,
  • Brigitte Leeners

摘要

Endometriosis is a chronic inflammatory condition marked by the presence of endometrial-like tissue outside the uterus, often co-occurring with leiomyoma and presenting a diagnostic challenge. We analyzed 96 plasma cytokines and inflammatory markers in 86 women undergoing surgery for suspected endometriosis, using multiplex assays and unsupervised clustering methods. Patients were classified using both rASRM and the more granular #Enzian system to assess lesion-specific and stage-specific biomarker patterns. We identified five distinct patient clusters reflecting disease heterogeneity, with improved resolution using the #Enzian classification. Notably, the presence of leiomyoma influenced cytokine profiles, potentially obscuring biomarker signals. Key biomarkers including perforin, TRAIL, sFasL, IL-17F, PDGF, VEGFA, and MCP-2 were associated with disease presence and stage. These findings highlight the value of advanced classification systems and emphasize the importance of accounting for comorbid conditions. Our results support the development of non-invasive biomarker panels for earlier and more accurate diagnosis of endometriosis.