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Multiplexed Fluorescent Immunohistochemical Staining of Four Endometrial Immune Cell Types in Recurrent Miscarriage
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sMICA/sMICB and Immune Checkpoint in Endometriosis: Toward a Minimally Invasive Diagnostic Model Based on Machine

Anastasia Belevich1, Maria Yarmolinskaya1, Ilya Smirnov1

  • 1Federal State Budgetary Scientific Institution, Research Institute of Obstetrics, Gynecology and Reproductology Named After D.O. Ott, 199034 St. Petersburg, Russia.

Biomedicines
|March 28, 2026
PubMed
Summary

This study reveals soluble MICA (sMICA) and MICA/B (sMICB) shedding are linked to endometriosis. Machine learning models using these markers and pain scores show promise for a novel, minimally invasive diagnostic approach.

Keywords:
MICAMICBendoglinendometriosisimmune checkpointsmachine learningminimally invasive diagnosis

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Area of Science:

  • Immunology
  • Oncology
  • Reproductive Medicine

Background:

  • Endometriosis significantly impacts women's quality of life and fertility.
  • Current diagnostic methods for endometriosis present challenges for clinicians.
  • Investigating cancer-like immune evasion mechanisms offers new insights into endometriosis.

Purpose of the Study:

  • To explore cancer-like immune evasion mechanisms in endometriosis.
  • To develop a novel diagnostic model for endometriosis using machine learning.
  • To identify potential biomarkers for endometriosis diagnosis.

Main Methods:

  • Measured soluble immune markers (sMICA, sMICB, etc.) in serum and peritoneal fluid.
  • Analyzed marker levels across endometriosis stages and associated conditions.
  • Developed and evaluated machine learning models (logistic regression, XGBoost) for diagnosis.

Main Results:

  • Peritoneal fluid sMICB levels varied with endometriosis stage and were higher with adhesions.
  • Elevated peritoneal fluid sMICA levels were observed in women with endometriosis-associated infertility.
  • Machine learning models, particularly XGBoost, achieved high accuracy (0.94-0.96) in diagnosing endometriosis, with serum sMICB and pain score as key predictors.

Conclusions:

  • Soluble MICA (sMICA) and MICA/B (sMICB) shedding play a role in endometriosis.
  • A novel, minimally invasive diagnostic approach for endometriosis is presented.
  • Machine learning models integrating immune markers and clinical data show diagnostic potential.