Identification of Potential Tissue Diagnostic Biomarkers for Endometriosis through Bioinformatics and Machine Learning Approaches with Experimental Validation

1

Endometriosis is a prevalent gynecological disorder with no reliable biomarkers, necessitating the identification of core diagnostic genes.
2

Four genes (COL6A3, BGN, LAMA4, THBS2) were identified as potential diagnostic markers, showing significant upregulation and high discriminatory power.
3

The study utilized bioinformatics and machine learning approaches, including WGCNA and ROC analysis, to refine and validate hub genes.
4

Immune dysregulation was observed in endometriosis, with specific immune cell types correlating with the identified hub genes.
5

The findings provide novel insights into the molecular mechanisms of endometriosis and suggest potential avenues for future research and clinical applications.

1