The Profiling Serum Cytokines as Stage-Specific Biomarkers for Endometriosis: A Cross-Sectional Study for Non-Invasive Staging
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A serum panel of IL-8, VEGF-A, and TNF-α accurately differentiates endometriosis stages, including early versus advanced disease, using machine learning on 40 quantified cytokines.
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This prospective cross-sectional study evaluated whether serum cytokines could non-invasively distinguish endometriosis patients by revised American Society for Reproductive Medicine (rASRM) stage and differentiate them from disease-free controls. Serum from 120 women with surgically and histologically confirmed endometriosis (Stage I–II vs III–IV) and 60 age-matched healthy controls was analyzed using a multiplex bead-based immunoassay measuring 40 cytokines, with Random Forest-based feature selection to build discriminatory panels. Eight cytokines differed across groups, and a combined panel of IL-8, VEGF-A, and TNF-α achieved an AUC of 0.94 for separating controls from endometriosis and an AUC of 0.87 for distinguishing early-stage from advanced-stage disease, with VEGF-A and IL-8 correlating positively with total rASRM scores. The paper’s main limitation is its cross-sectional design, assessing biomarkers at a single timepoint rather than validating their performance prospectively for staging. This paper is centrally about endometriosis — it identifies a serum cytokine signature for non-invasive detection and rASRM stage discrimination in endometriosis.
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Abstract
Background: Endometriosis staging according to the revised American Society for Reproductive Medicine (rASRM) criteria relies on invasive laparoscopic surgery. The development of a reliable, non-invasive method to assess disease severity remains a critical unmet clinical need. Serum cytokines, as mediators of systemic inflammation, represent promising candidate biomarkers for this purpose.Objective: To identify and validate a panel of serum cytokines capable of distinguishing between different rASRM stages of endometriosis and differentiating patients from disease-free controls.Methods: In this prospective cross-sectional study, we enrolled 180 participants: 120 women with surgically and histologically confirmed endometriosis (stratified into Stage I-II, n=60; Stage III-IV, n=60) and 60 healthy, age-matched controls. A multiplex bead-based immunoassay was used to quantify 40 cytokines in serum samples. Machine learning algorithms, primarily Random Forest, were employed for feature selection and the development of a discriminatory biomarker panel.Results: Univariate analysis identified eight cytokines with significant differential expression across groups: IL-8, TNF-α, VEGF-A, IL-1β, MCP-1, IL-10, IL-17A, and IFN-γ. A combined panel of IL-8, VEGF-A, and TNF-α demonstrated superior discriminatory power. This panel differentiated controls from all endometriosis patients with an area under the curve (AUC) of 0.94 (95% CI: 0.90–0.98). Most notably, it distinguished early-stage (I-II) from advanced-stage (III-IV) disease with an AUC of 0.87 (95% CI: 0.81–0.93). Serum levels of VEGF-A and IL-8 showed strong positive correlations with total rASRM scores.Conclusion: We have identified a novel three-cytokine serum signature (IL-8, VEGF-A, TNF-α) with high diagnostic accuracy for both the detection and surgical staging of endometriosis. This "liquid biopsy" approach holds significant potential to revolutionize patient management by enabling non-invasive triage, personalizing therapeutic strategies, and reducing diagnostic delay.
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Abstract
Background: Endometriosis staging according to the revised American Society for Reproductive Medicine (rASRM) criteria relies on invasive laparoscopic surgery. The development of a reliable, non-invasive method to assess disease severity remains a critical unmet clinical need. Serum cytokines, as mediators of systemic inflammation, represent promising candidate biomarkers for this purpose.
Objective
To identify and validate a panel of serum cytokines capable of distinguishing between different rASRM stages of endometriosis and differentiating patients from disease-free controls.
Methods
In this prospective cross-sectional study, we enrolled 180 participants: 120 women with surgically and histologically confirmed endometriosis (stratified into Stage I-II, n=60; Stage III-IV, n=60) and 60 healthy, age-matched controls. A multiplex bead-based immunoassay was used to quantify 40 cytokines in serum samples. Machine learning algorithms, primarily Random Forest, were employed for feature selection and the development of a discriminatory biomarker panel.
Results
Univariate analysis identified eight cytokines with significant differential expression across groups: IL-8, TNF-α, VEGF-A, IL-1β, MCP-1, IL-10, IL-17A, and IFN-γ. A combined panel of IL-8, VEGF-A, and TNF-α demonstrated superior discriminatory power. This panel differentiated controls from all endometriosis patients with an area under the curve (AUC) of 0.94 (95% CI: 0.90–0.98). Most notably, it distinguished early-stage (I-II) from advanced-stage (III-IV) disease with an AUC of 0.87 (95% CI: 0.81–0.93). Serum levels of VEGF-A and IL-8 showed strong positive correlations with total rASRM scores.
Conclusion
We have identified a novel three-cytokine serum signature (IL-8, VEGF-A, TNF-α) with high diagnostic accuracy for both the detection and surgical staging of endometriosis. This "liquid biopsy" approach holds significant potential to revolutionize patient management by enabling non-invasive triage, personalizing therapeutic strategies, and reducing diagnostic delay.
References
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