Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Reproductive Sciences and Sexual Health Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
J Assist Reprod Genet. 2023 Oct;40(10):2439-2451. doi: 10.1007/s10815-023-02903-y. Epub 2023 Aug 9.
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.
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.
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.
In conclusion, this study identified potential diagnostic and therapeutic targets for EMs which demand more experimental confirmations.
子宫内膜异位症（EMs）是女性的一种主要妇科疾病。由于缺乏明确的症状，其早期检测极具挑战性；因此，寻找生物标志物以简化其诊断和治疗至关重要。在这里，我们通过构建调控网络和使用机器学习方法，旨在确定 EMs 的潜在诊断和治疗靶点。
合并了三个基因表达综合（GEO）数据集，并在预处理步骤后鉴定差异表达基因（DEGs）。使用 DEGs，构建了转录因子（TF）-mRNA-miRNA 调控网络，并基于 CytoHubba 中的四种不同算法检测了枢纽基因。使用枢纽基因构建了高斯 NB 诊断模型，并使用 Discovery Studio 和 AutoDock Vina 软件进行对接分析。
共鉴定了 119 个 EMs 和非 EMs 样本之间的差异表达基因。然后构建了一个由 52 个 mRNAs、249 个 miRNAs 和 37 个 TFs 组成的调控网络。使用从网络中选择的枢纽基因（GATA6、HMOX1、HS3ST1、NFASC 和 PTGIS）构建了诊断模型，其在训练和验证队列中的曲线下面积（AUC）分别为 0.98 和 0.92。基于对接分析，两种化学化合物，罗非昔布和维甲酸，对 EMs 具有潜在的治疗作用。
总之，本研究确定了 EMs 的潜在诊断和治疗靶点，需要更多的实验验证。
