WISP2/CCN5 revealed as a potential diagnostic biomarker for endometriosis based on machine learning and single-cell transcriptomic analysis.
Journal:
Functional & integrative genomics
Published Date:
Jun 19, 2025
Abstract
OBJECTIVE: Endometriosis is a prevalent gynecological disease characterized by the ectopic growth of functional endometrial tissue outside the uterine cavity, affecting millions of women worldwide. Currently, the definitive diagnosis relies on invasive laparoscopy (the gold standard), with an average diagnostic delay of 7-10 years from symptom onset. Non-invasive biomarkers from blood or endometrial samples could enable early screening and reduce diagnostic time. Emerging technologies like single-cell sequencing and transcriptomics offer promising approaches for identifying highly specific biomarkers, advancing endometriosis research into the precision medicine era.
