A Combined Transcriptomic and Machine Learning Study Reveals PAX8 as a Promising Diagnostic Biomarker in...

Area of Science:

• Genomics
• Biomarker Discovery
• Computational Biology

Background:

• Endometriosis (EM) is a chronic, estrogen-dependent condition.
• Current diagnostic methods for EM lack reliable noninvasive biomarkers.

Purpose of the Study:

• To evaluate the diagnostic potential of PAX8 for endometriosis.
• To utilize integrated transcriptomic and machine learning analyses for biomarker discovery.

Main Methods:

• Differential gene expression analysis on GSE141549 dataset.
• Weighted gene coexpression network analysis (WGCNA).
• Machine learning models (Random Forest, SVM, Logistic Regression) with cross-validation.

Main Results:

• Identified 887 differentially expressed genes (DEGs).
• PAX8 was significantly downregulated in ectopic tissues and identified as a key diagnostic feature.
• PAX8-related gene sets were enriched in cilium organization, wound healing, calcium signaling, JAK-STAT signaling, and focal adhesion pathways.

Conclusions:

• PAX8 shows promise as a noninvasive diagnostic biomarker for endometriosis.
• Transcriptomic and machine learning approaches can effectively identify diagnostic markers for EM.
• PAX8 may play a role in the immunomodulatory aspects of endometriosis.