Abstract
Endometriosis (EM), an estrogen-dependent inflammatory disease with unknown etiology, affects thousands of childbearing-age couples, and its early diagnosis is still very difficult. With the rapid development of sequencing technology in recent years, the accumulation of many sequencing data makes it possible to screen important diagnostic biomarkers from some EM-related genes. In this study, we utilized public datasets in the Gene Expression Omnibus (GEO) and Array-Express database and identified seven important differentially expressed genes (DEGs) (COMT, NAA16, CCDC22, EIF3E, AHI1, DMXL2, and CISD3) through the random forest classifier. Among these DEGs, AHI1, DMXL2, and CISD3 have never been reported to be associated with the pathogenesis of EMs. Our study indicated that these three genes might participate in the pathogenesis of EMs through oxidative stress, epithelial–mesenchymal transition (EMT) with the activation of the Notch signaling pathway, and mitochondrial homeostasis, respectively. Then, we put these seven DEGs into an artificial neural network to construct a novel diagnostic model for EMs and verified its diagnostic efficacy in two public datasets. Furthermore, these seven DEGs were included in 15 hub genes identified from the constructed protein–protein interaction (PPI) network, which confirmed the reliability of the diagnostic model. We hope the diagnostic model can provide novel sights into the understanding of the pathogenesis of EMs and contribute to the clinical diagnosis and treatment of EMs.
Highlights
Endometriosis (EM) is an estrogen-dependent inflammatory disorder, which afflicts about 10%–15% of women of childbearing age (Parasar et al, 2017)
We firstly identified some differentially expressed genes (DEGs) between EMs and normal samples from public datasets in the Gene Expression Omnibus (GEO) database
We used the “removeBatchEffect” function in the R package “LIMMA” to adjust batch effects and conducted principal component analysis (PCA) analysis to evaluate the performance of batch effect adjustment
Summary
Endometriosis (EM) is an estrogen-dependent inflammatory disorder, which afflicts about 10%–15% of women of childbearing age (Parasar et al, 2017). Sampson’s theory of menstrual blood reflux observed in most patients is commonly accepted in the pathophysiology of EMs, while only a small portion will develop into this disease (Burney and Giudice, 2012). It could only explain a portion of EMs. it’s necessary to further investigate a comprehensive understanding of the pathogenesis of EMs and find effective molecular biomarkers to improve the early diagnosis and treatment of EMs
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