ArticleJournal of assisted reproduction and genetics2023
Identification of potential diagnostic biomarkers and therapeutic targets for endometriosis based on bioinformatics and machine learning analysis.
Article in Journal of assisted reproduction and genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- Identifying Predictive Biomarkers and Immune Infiltration Features in Endometriosis.Reproductive sciences (Thousand Oaks, Calif.) · 2026Article
- A new perspective on endometriosis: Integrating eQTL mendelian randomization with transcriptomics and single-cell data analyses.Functional & integrative genomics · 2025Article
- Plasma microRNA expression in adolescents and young adults with endometriosis: the importance of hormone use.Frontiers in reproductive health · 2024Article
- The Pathological Role of miRNAs in Endometriosis.Biomedicines · 2023Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeEndometriosis (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.
methodsThree 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.
resultsA 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.
conclusionIn conclusion, this study identified potential diagnostic and therapeutic targets for EMs which demand more experimental confirmations.
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