Aims
To explore the diagnostic biomarkers for diagnosing endometriosis. Background: Endometriosis is a benign, progressive, estrogen-dependent gynecological disorder that has highly variant prevalence. Therefore, it is essential to develop reliable diagnostic biomarkers for endometriosis diagnosis. Objective: To explore the diagnostic biomarkers for endometriosis diagnosis. Method: Based on transcriptome data from GSE145701, we identified potential therapeutic targets through the intersection of endometriosis-related genes from weighted gene correlation network analysis (WGCNA) and differential expression analysis. Aprotein-protein interaction (PPI) was constructed. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were employed for functional enrichment analysis. The intersection of hub genes from topological analysis and module genes from module-based network analysis were selected as core targets, which were used for diagnostic model construction. Its robustness was validated using GSE7305 and GSE134056. Associations of core targets with immune characteristics and pathways were further evaluated. Molecular docking was employed to evaluate the docking affinity between core targets and drugs. Additionally, western blot and quantitative real-time polymerase chain reaction were also carried out to validate molecular docking
Background
Endometriosis is a benign, progressive, estrogen-dependent gynecological disorder that has highly variant prevalence. Therefore, it is essential to develop reliable diagnostic biomarkers for endometriosis diagnosis.
Conclusion
These results may facilitate the in-depth understanding of the development of endometriosis, and guide early diagnostic as well as clinical treatments for patients with endometriosis.
Objective
To explore the diagnostic biomarkers for endometriosis diagnosis. Method: Based on transcriptome data from GSE145701, we identified potential therapeutic targets through the intersection of endometriosis-related genes from weighted gene correlation network analysis (WGCNA) and differential expression analysis. Aprotein-protein interaction (PPI) was constructed. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were employed for functional enrichment analysis. The intersection of hub genes from topological analysis and module genes from module-based network analysis were selected as core targets, which were used for diagnostic model construction. Its robustness was validated using GSE7305 and GSE134056. Associations of core targets with immune characteristics and pathways were further evaluated. Molecular docking was employed to evaluate the docking affinity between core targets and drugs. Additionally, western blot and quantitative real-time polymerase chain reaction were also carried out to validate molecular docking