Abstract：
The purpose of this study was to screen differentially expressed genes in PCOS using gene chip data and investigate the biological functions of these DEGs in PCOS. Additionally, the study aimed to analyze the potential clinical significance of these genes using clinical data. In this study, we first screened the DEGs related to PCOS by using the gene chip data (GSE5090) from GEO database. Target gene prediction software was used to predict the target genes for these DEGs, and their functional enrichment was analysed. Subsequently, the STRING online tool and Cytoscape software were utilized to identify key genes by constructing protein-protein interaction networks (PPI). In the analysis of the GSE5090 dataset, seventeen differentially expressed genes (DEGs) were identified. Functional enrichment analysis revealed that these DEGs are predominantly associated with biological functions related to polycystic ovary syndrome (PCOS). Moreover, the tissue-specific expression analysis highlighted immune system markers, with a notable difference observed in 18 of these markers, accounting for 20.5% of the total. By constructing PPI networks and key gene regulatory networks, a total of three genes (RPL13, LEP, and ANXA1) were identified as key genes. In addition, the column-line graphical model performed well in predicting the risk of PCOS. Using ROC curves, the model proved to be effective in diagnosis. This study represents the first application of a bioinformatics approach to identify and confirm high expression levels of RPL13, LEP, and ANXA1 in patients with Polycystic Ovary Syndrome (PCOS). These key genes-RPL13, LEP, and ANXA1-may present viable targets for therapeutic interventions in PCOS, underscoring their potential clinical importance.
摘要：
本研究的目的是使用基因芯片数据筛选PCOS中差异表达的基因，并研究这些DEGs在PCOS中的生物学功能。此外,本研究旨在利用临床数据分析这些基因的潜在临床意义。在这项研究中,我们首先利用GEO数据库的基因芯片数据(GSE5090)筛选与PCOS相关的DEGs。靶基因预测软件用于预测这些DEG的靶基因，并对其功能富集进行了分析。随后，利用STRING在线工具和Cytoscape软件通过构建蛋白质-蛋白质相互作用网络(PPI)来鉴定关键基因.在对GSE5090数据集的分析中,鉴定出17个差异表达基因(DEGs)。功能富集分析表明，这些DEGs主要与多囊卵巢综合征（PCOS）相关的生物学功能有关。此外，组织特异性表达分析突出了免疫系统标志物，在这些标记中的18个中观察到显著差异，占总数的20.5%。通过构建PPI网络和关键基因调控网络,总共三个基因(RPL13，LEP，和ANXA1)被鉴定为关键基因。此外,柱线图形模型在预测PCOS风险方面表现良好.使用ROC曲线，该模型被证明是有效的诊断。这项研究代表了生物信息学方法的首次应用，以识别和确认RPL13，LEP，多囊卵巢综合征（PCOS）患者的ANXA1。这些关键基因-RPL13，LEP，和ANXA1-可能为PCOS的治疗干预提供可行的目标，强调其潜在的临床重要性。
