Related Experiment Video
Updated: Jul 19, 2025
Establishment of an Experimental Mouse Model of Endometrioma to Study its Related Infertility
Published on: April 5, 2024
Identification of potential diagnostic biomarkers and therapeutic targets for endometriosis based on bioinformatics
Maryam Hosseini1, Behnaz Hammami1, Mohammad Kazemi2,3
1Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Purpose:
Endometriosis (EMs) is a major gynecological condition in women. Due to the absence of definitive symptoms, its early detection is very challenging; thus, it is crucial to find biomarkers to ease its diagnosis and therapy. Here, we aimed to identify potential diagnostic and therapeutic targets for EMs by constructing a regulatory network and using machine learning approaches.
Methods:
Three Gene Expression Omnibus (GEO) datasets were merged, and differentially expressed genes (DEGS) were identified after preprocessing steps. Using the DEGs, a transcription factor (TF)-mRNA-miRNA regulatory network was constructed, and hub genes were detected based on four different algorithms in CytoHubba. The hub genes were used to build a GaussianNB diagnostic model and also in docking analysis that were performed using Discovery Studio and AutoDock Vina software.
Results:
A total of 119 DEGs were identified between EMs and non-EMs samples. A regulatory network consisting of 52 mRNAs, 249 miRNAs, and 37 TFs was then constructed. The diagnostic model was introduced using the hub genes selected from the network (GATA6, HMOX1, HS3ST1, NFASC, and PTGIS) that its area under the curve (AUC) was 0.98 and 0.92 in the training and validation cohorts, respectively. Based on docking analysis, two chemical compounds, rofecoxib and retinoic acid, had potential therapeutic effects on EMs.
Conclusion:
In conclusion, this study identified potential diagnostic and therapeutic targets for EMs which demand more experimental confirmations.
