Master's Programme in Biomedical Sciences, Faculty of Medicine of Universitas Indonesia, Jakarta 10430, Indonesia; Department of Medical Biology, Faculty of Medicine of Universitas Indonesia, Jakarta 10430, Indonesia.
Department of Medical Chemistry, Faculty of Medicine of Universitas Indonesia, Jakarta 10430, Indonesia.
Eur J Obstet Gynecol Reprod Biol. 2024 Nov;302:216-224. doi: 10.1016/j.ejogrb.2024.09.026. Epub 2024 Sep 19.
Endometriosis is a common gynecological disease with a significant economic burden. Growing evidence has suggested the role of aberrant gene expression and epigenetic mechanisms in the pathogenesis of endometriosis. This study aims to identify potential key genes, epigenetic features, and regulatory networks in endometriosis using an integrated bioinformatic approach.
Six microarray and RNA-sequencing datasets (GSE23339, GSE7305, GSE25628, GSE51981, GSE120103, GSE87809) were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) of each dataset were analyzed using the GEO2R tool, and their mRNA, miRNA, and lncRNA components were identified subsequently. The common DEGs between datasets were combined, and the Gene ontology (GO) and pathway enrichment were analyzed using the ShinyGo. The protein-protein interaction (PPI) network of DEGs, miRNA, and lncRNA was constructed using STRING and Cytoscape, and then the top 15 hub genes in the PPI network were identified using CytoHubba.
A total of 551 common DEGs were identified from four or more studies, including 292 upregulated and 259 downregulated genes. Besides alterations in protein-coding genes (mRNA), 16 miRNA (5 upregulated and 11 downregulated) were identified from all studies, along with 12 lncRNA (10 upregulated and 2 downregulated) that were common in at least three studies. Enriched DEGs were mainly associated with extracellular matrix (ECM) interaction, P53 signaling pathway, and focal adhesion, which are suggested to play vital roles in the pathogenesis of endometriosis. Through PPI network construction of common DEGs, 178 nodes and 683 edges were obtained, from which 15 hub genes were identified, including CDK1, CCNB1, KIF11, CCNA2, BUB1B, DLGAP5, BUB1, TOP2A, ASPM, CEP55, CENPF, TPX2, CCNB2, KIFC, NCAPG.
Our in-depth bioinformatics analysis reveals the critical molecular basis underlying endometriosis. The role of identified hub genes, miRNA, and lncRNA may also have an opportunity to be explored as potential biomarkers for endometriosis diagnosis and prognosis.
子宫内膜异位症是一种常见的妇科疾病，具有显著的经济负担。越来越多的证据表明，异常基因表达和表观遗传机制在子宫内膜异位症的发病机制中起作用。本研究旨在通过综合生物信息学方法，鉴定子宫内膜异位症中的潜在关键基因、表观遗传特征和调控网络。
从基因表达综合数据库（GEO）下载了六个微阵列和 RNA-seq 数据集（GSE23339、GSE7305、GSE25628、GSE51981、GSE120103、GSE87809）。使用 GEO2R 工具分析每个数据集的差异表达基因（DEG），随后鉴定其 mRNA、miRNA 和 lncRNA 成分。合并数据集之间的共同 DEG，使用 ShinyGo 进行基因本体论（GO）和通路富集分析。使用 STRING 和 Cytoscape 构建 DEG、miRNA 和 lncRNA 的蛋白质-蛋白质相互作用（PPI）网络，然后使用 CytoHubba 识别 PPI 网络中的前 15 个枢纽基因。
从四个或更多研究中鉴定出 551 个共同的 DEG，包括 292 个上调和 259 个下调基因。除了蛋白质编码基因（mRNA）的改变外，还从所有研究中鉴定出 16 个 miRNA（5 个上调和 11 个下调）和 12 个 lncRNA（至少三个研究中共同的 10 个上调和 2 个下调）。富集的 DEG 主要与细胞外基质（ECM）相互作用、P53 信号通路和焦点黏附有关，这些通路被认为在子宫内膜异位症的发病机制中起着重要作用。通过共同 DEG 的 PPI 网络构建，获得了 178 个节点和 683 个边缘，从中鉴定出 15 个枢纽基因，包括 CDK1、CCNB1、KIF11、CCNA2、BUB1B、DLGAP5、BUB1、TOP2A、ASPM、CEP55、CENPF、TPX2、CCNB2、KIFC、NCAPG。
我们深入的生物信息学分析揭示了子宫内膜异位症的关键分子基础。鉴定出的枢纽基因、miRNA 和 lncRNA 的作用也可能有机会作为子宫内膜异位症诊断和预后的潜在生物标志物进行探索。
