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Volume 31, Issue 41
  • ISSN: 0929-8673
  • E-ISSN: 1875-533X

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 CDH 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 CDH 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. 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.

© 2024 Bentham Science Publishers
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2024-01-03
2024-11-20

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/content/journals/cmc/10.2174/0109298673283426231220100011
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Development and Validation of the Diagnostic Model of 7 Gene in Endometriosis
Curr. Med. Chem. 31, 6871 (2024); https://doi.org/10.2174/0109298673283426231220100011
/content/journals/cmc/10.2174/0109298673283426231220100011
/content/journals/cmc/10.2174/0109298673283426231220100011
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  • Article Type:     Research Article
Keyword(s): diagnosis; Endometriosis; module-based network analysis; molecular docking; protein-protein interactions network; topological analysis

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