Development and Validation of the Diagnostic Model of 7 Gene in Endometriosis
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This study identified seven core targets and constructed a diagnostic model with high accuracy for endometriosis, revealing correlations with immune characteristics, metabolic pathways, and potential drug targets.
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Abstract
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.
METHODS: 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 results.
RESULTS: A diagnostic model was constructed using 7 core targets, which had a high diagnostic ability for endometriosis. CTSK was positively correlated with immune scores, while CDH2 was negatively correlated with immune scores. CTSK, HGF, and EPCAM were positively correlated with energy metabolism and inflammation-related pathways, while RUNX2, FN1, NCAM1, and CDH2 were positively correlated with epithelial-to-mesenchymal transition (EMT) and unfolded protein response (UPR). Moreover, FN1 had good docking affinity with Elagolix, Esmya, and Proellex. NCAM1 might be a promising target modulated by Elagolix. In vitro experiment revealed that the expression of FN1 in human normal endometrial cell lines (hEEC) gradually decreased with the increase of Esmya concentration, indicating that FN1 was a target for Esmya.
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.
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Abstract
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.
Methods
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 results.
Results
A diagnostic model was constructed using 7 core targets, which had a high diagnostic ability for endometriosis. CTSK was positively correlated with immune scores, while CDH2 was negatively correlated with immune scores. CTSK, HGF, and EPCAM were positively correlated with energy metabolism and inflammation-related pathways, while RUNX2, FN1, NCAM1, and CDH2 were positively correlated with epithelial-to-mesenchymal transition (EMT) and unfolded protein response (UPR). Moreover, FN1 had good docking affinity with Elagolix, Esmya, and Proellex. NCAM1 might be a promising target modulated by Elagolix. In vitro experiment revealed that the expression of FN1 in human normal endometrial cell lines (hEEC) gradually decreased with the increase of Esmya concentration, indicating that FN1 was a target for Esmya.
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.
Keywords
Endometriosis, diagnosis, protein-protein interactions network, module-based network analysis, topological analysis, molecular docking.
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Cited by (2)
- Integration of Single Cell and Bulk RNA-Sequencing Reveals Key Genes and Immune Cell Infiltration to Construct a Predictive Model and Identify Drug Targets in Endometriosis 2025
- From gut-reproductive microbiota to ferroptosis: a comprehensive insight into the molecular-pathogenicity of endometriosis 2026
Cited by (2)
- From gut-reproductive microbiota to ferroptosis: a comprehensive insight into the molecular-pathogenicity of endometriosis 2026
- Integration of Single Cell and Bulk RNA-Sequencing Reveals Key Genes and Immune Cell Infiltration to Construct a Predictive Model and Identify Drug Targets in Endometriosis 2025
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