Unveiling Key Biomarkers and Therapeutic Drugs in Polycystic Ovary Syndrome (PCOS) Through Pathway Enrichment Analysis and Hub Gene-miRNA Networks.

IF 1.8 4区 医学 Q3 PHARMACOLOGY & PHARMACY Iranian Journal of Pharmaceutical Research Pub Date : 2023-11-20 eCollection Date: 2023-01-01 DOI:10.5812/ijpr-139985
Roozbeh Heidarzadehpilehrood, Maryam Pirhoushiaran, Malina Binti Osman, King-Hwa Ling, Habibah Abdul Hamid
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Abstract

Background: 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.

Objectives: 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.

Methods: 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.

Results: 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 hsa-miR-8085, hsa-miR-548w, hsa-miR-612, hsa-miR-1470, and hsa-miR-644a, demonstrated interactions with the 10 hub genes in the hub gene-miRNA networks in our study. Except for hsa-miR-612, the other 4 DE-miRNAs, including hsa-miR-8085, hsa-miR-548w, hsa-miR-1470, and hsa-miR-644a, are novel and had not been reported in PCOS pathogenesis before. Also, GO and pathway enrichment analyses identified "pathogenic E. coli 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 ACTB, JUN, PTEN, KRAS, and MAPK1 as potential targets to treat PCOS with therapeutic drugs.

Conclusions: The findings from this study might assist researchers in uncovering new biomarkers and potential therapeutic drug targets in PCOS treatment.

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通过通路富集分析和枢纽基因-miRNA 网络揭示多囊卵巢综合征 (PCOS) 的关键生物标记物和治疗药物。
背景:多囊卵巢综合征(PCOS多囊卵巢综合征(PCOS)影响着全球育龄妇女,发病率为 5%-26%。越来越多的证据表明,微小RNA(miRNA)在多囊卵巢综合征颗粒细胞(GCs)的病理生理学中发挥着重要作用:本研究的目的是通过分析三个不同的微阵列数据集,确定在枢纽基因-miRNA网络中差异表达最高的miRNA(DE-miRNA)及其相应的靶点,并确定新的DE-miRNA。此外,还利用生物信息学方法进行了功能富集分析。最后,研究了 5 个排名靠前的中心基因与药物之间的相互作用:使用生物信息学方法分析了基因表达总库(GEO)中的三个 GC 图谱,即基因表达总库系列(GSE)-34526、GSE114419 和 GSE137684。使用 multiMiR R 软件包预测了顶级 DE-miRNA 的靶标,只检索了结果有效的 miRNA。将 "DE-miRNA 预测结果 "与 "现有组织 DE-mRNA "之间的共同基因指定为差异表达基因(DEG)。对 DEGs 进行了基因本体(GO)和通路富集分析。为了确定枢纽基因和枢纽 DE-miRNA,使用 Cytoscape 软件构建了蛋白质-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)来确定排名靠前的中心基因与药物之间的相互作用:结果: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"。在调查的 13 个 DE-miRNA 中,只有 5 个(即 hsa-miR-8085、hsa-miR-548w、hsa-miR-612、hsa-miR-1470 和 hsa-miR-644a)在我们的研究中与枢纽基因-miRNA 网络中的 10 个枢纽基因发生了相互作用。除hsa-miR-612外,其他4个DE-miRNA,包括hsa-miR-8085、hsa-miR-548w、hsa-miR-1470和hsa-miR-644a,都是新的DE-miRNA,以前从未在PCOS发病机制中报道过。此外,GO和通路富集分析发现,京都基因和基因组百科全书(KEGG)中的 "致病性大肠杆菌感染 "和FunRich中的 "Rac1活性调控 "是最重要的通路。药物-枢纽基因相互作用网络将ACTB、JUN、PTEN、KRAS和MAPK1确定为使用治疗药物治疗多囊卵巢综合征的潜在靶点:本研究的发现可能有助于研究人员发现治疗多囊卵巢综合症的新生物标志物和潜在治疗药物靶点。
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来源期刊
CiteScore
3.40
自引率
6.20%
发文量
52
审稿时长
2 months
期刊介绍: The Iranian Journal of Pharmaceutical Research (IJPR) is a peer-reviewed multi-disciplinary pharmaceutical publication, scheduled to appear quarterly and serve as a means for scientific information exchange in the international pharmaceutical forum. Specific scientific topics of interest to the journal include, but are not limited to: pharmaceutics, industrial pharmacy, pharmacognosy, toxicology, medicinal chemistry, novel analytical methods for drug characterization, computational and modeling approaches to drug design, bio-medical experience, clinical investigation, rational drug prescribing, pharmacoeconomics, biotechnology, nanotechnology, biopharmaceutics and physical pharmacy.
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