Heidarzadehpilehrood Roozbeh, Pirhoushiaran Maryam, Binti Osman Malina, Ling King-Hwa, Abdul Hamid Habibah
Department of Obstetrics & Gynaecology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia.
Department of Medical Genetics, School of Medicine, Tehran University of Medical Sciences, 1417613151, Tehran, Iran.
Iran J Pharm Res. 2023 Nov 20;22(1):e139985. doi: 10.5812/ijpr-139985. eCollection 2023 Jan-Dec.
Polycystic ovary syndrome (PCOS) affects women of reproductive age globally with an incidence rate of 5% - 26%. Growing evidence reports important roles for microRNAs (miRNAs) in the pathophysiology of granulosa cells (GCs) in PCOS.
The objectives of this study were to identify the top differentially expressed miRNAs (DE-miRNAs) and their corresponding targets in hub gene-miRNA networks, as well as identify novel DE-miRNAs by analyzing three distinct microarray datasets. Additionally, functional enrichment analysis was performed using bioinformatics approaches. Finally, interactions between the 5 top-ranked hub genes and drugs were investigated.
Using bioinformatics approaches, three GC profiles from the gene expression omnibus (GEO), namely gene expression omnibus series (GSE)-34526, GSE114419, and GSE137684, were analyzed. Targets of the top DE-miRNAs were predicted using the multiMiR R package, and only miRNAs with validated results were retrieved. Genes that were common between the "DE-miRNA prediction results" and the "existing tissue DE-mRNAs" were designated as differentially expressed genes (DEGs). Gene ontology (GO) and pathway enrichment analyses were implemented for DEGs. In order to identify hub genes and hub DE-miRNAs, the protein-protein interaction (PPI) network and miRNA-mRNA interaction network were constructed using Cytoscape software. The drug-gene interaction database (DGIdb) database was utilized to identify interactions between the top-ranked hub genes and drugs.
Out of the top 20 DE-miRNAs that were retrieved from the GSE114419 and GSE34526 microarray datasets, only 13 of them had "validated results" through the multiMiR prediction method. Among the 13 DE-miRNAs investigated, only 5, namely , , , , and , demonstrated interactions with the 10 hub genes in the hub gene-miRNA networks in our study. Except for , the other 4 DE-miRNAs, including , , , and , are novel and had not been reported in PCOS pathogenesis before. Also, GO and pathway enrichment analyses identified "pathogenic infection" in the Kyoto encyclopedia of genes and genomes (KEGG) and "regulation of Rac1 activity" in FunRich as the top pathways. The drug-hub gene interaction network identified , , , , and as potential targets to treat PCOS with therapeutic drugs.
The findings from this study might assist researchers in uncovering new biomarkers and potential therapeutic drug targets in PCOS treatment.
多囊卵巢综合征(PCOS)在全球影响着育龄女性,发病率为5% - 26%。越来越多的证据表明,微小RNA(miRNA)在PCOS颗粒细胞(GCs)的病理生理学中发挥着重要作用。
本研究的目的是在枢纽基因 - miRNA网络中鉴定差异表达最显著的miRNA(DE - miRNA)及其相应靶点,并通过分析三个不同的微阵列数据集鉴定新的DE - miRNA。此外,使用生物信息学方法进行功能富集分析。最后,研究排名前5的枢纽基因与药物之间的相互作用。
使用生物信息学方法,分析了来自基因表达综合数据库(GEO)的三个GC谱,即基因表达综合系列(GSE) - 34526、GSE114419和GSE137684。使用multiMiR R包预测排名前的DE - miRNA的靶点,仅检索具有验证结果的miRNA。“DE - miRNA预测结果”与“现有组织DE - mRNA”之间共有的基因被指定为差异表达基因(DEG)。对DEG进行基因本体(GO)和通路富集分析。为了鉴定枢纽基因和枢纽DE - miRNA,使用Cytoscape软件构建蛋白质 - 蛋白质相互作用(PPI)网络和miRNA - mRNA相互作用网络。利用药物 - 基因相互作用数据库(DGIdb)数据库鉴定排名前的枢纽基因与药物之间的相互作用。
从GSE114419和GSE34526微阵列数据集中检索到的前20个DE - miRNA中,只有13个通过multiMiR预测方法获得了“验证结果”。在研究的13个DE - miRNA中,只有5个,即[具体名称未给出],在我们研究的枢纽基因 - miRNA网络中与10个枢纽基因表现出相互作用。除了[具体名称未给出],其他4个DE - miRNA,包括[具体名称未给出],是新发现的,之前在PCOS发病机制中未被报道过。此外,GO和通路富集分析确定京都基因与基因组百科全书(KEGG)中的“致病性感染”和FunRich中的“Rac1活性调节”为主要通路。药物 - 枢纽基因相互作用网络确定[具体名称未给出]为用治疗药物治疗PCOS的潜在靶点。
本研究结果可能有助于研究人员在PCOS治疗中发现新的生物标志物和潜在的治疗药物靶点。