Table 6_Exploration of key genes associated with oxidative stress in polycystic ovary syndrome and experimental validation.xls

IntroductionThe current study demonstrated that oxidative stress (OS) is closely related to the pathogenesis of polycystic ovary syndrome (PCOS). However, there are numerous factors that lead to OS, therefore, identifying the key genes associated with PCOS that contribute to OS is crucial for elucidating the pathogenesis of PCOS and selecting appropriate treatment strategies.
MethodsFour datasets (GSE95728, GSE106724, GSE138572, and GSE145296) were downloaded from the gene expression omnibus (GEO) database. GSE95728 and GSE106724 were combined to identify differentially expressed genes (DEGs) in PCOS. weighted gene correlation network analysis (WGCNA) was used to screen key module genes associated with PCOS. Differentially expressed OS related genes (DE-OSRGs) associated with PCOS were obtained by overlapping DEGs, key module genes, and OSRGs. Subsequently, the optimal machine model was obtained to identify key genes by comparing the performance of the random forest model (RF), support vector machine model (SVM), and generalized linear model (GLM). The molecular networks were constructed to reveal the non-coding regulatory mechanisms of key genes based on GSE138572 and GSE145296. The Drug-Gene Interaction Database (DGIdb) was used to predict the potential therapeutic agents of key genes for PCOS. Finally, the expression of key OSRGs was validated by RT-PCR.
ResultsIn this study, 8 DE-OSRGs were identified. Based on the residuals and root mean square error of the three models, the best performance of RF was derived and 7 key genes (TNFSF10, CBL, IFNG, CP, CASP8, APOA1, and DDIT3) were identified. The GSEA enrichment analysis revealed that TNFSF10, CP, DDIT3, and INFG are all enriched in the NOD-like receptor signaling pathway and natural killer cell-mediated cytotoxicity pathways. The molecular regulatory network uncovered that both TNFSF10 and CBL are regulated by non-coding RNAs. Additionally, 70 potential therapeutic drugs for PCOS were predicted, with ibuprofen associated with DDIT3 and IFNG. RT-qPCR validation confirmed the expression trends of key genes IFNG, DDIT3, and APOA1 were consistent with the dataset, and the observed differences were statistically significant (P < 0.05).
ConclusionThe identification of seven key genes and molecular regulatory networks through bioinformatics analysis is of great significance for exploring the pathogenesis and therapeutic strategies of PCOS.

引言
本研究证实，氧化应激（oxidative stress, OS）与多囊卵巢综合征（polycystic ovary syndrome, PCOS）的发病机制密切相关。由于氧化应激的诱发因素繁多，因此筛选与PCOS相关且参与氧化应激过程的关键基因，对于阐明PCOS的发病机制、制定合理的治疗策略具有重要意义。

方法
从基因表达综合（Gene Expression Omnibus, GEO）数据库下载了4组数据集（GSE95728、GSE106724、GSE138572及GSE145296）。将GSE95728与GSE106724合并，以筛选PCOS相关的差异表达基因（differentially expressed genes, DEGs）；采用加权基因共表达网络分析（weighted gene correlation network analysis, WGCNA）筛选与PCOS相关的关键模块基因。通过将DEGs、关键模块基因与氧化应激相关基因（OSRGs）取交集，获得PCOS相关的差异表达氧化应激相关基因（differentially expressed OS related genes, DE-OSRGs）。随后，通过对比随机森林模型（random forest model, RF）、支持向量机模型（support vector machine model, SVM）及广义线性模型（generalized linear model, GLM）的性能，筛选出最优机器学习模型以鉴定关键基因。基于GSE138572与GSE145296数据集构建分子调控网络，以揭示关键基因的非编码RNA调控机制。借助药物-基因相互作用数据库（Drug-Gene Interaction Database, DGIdb）预测PCOS关键基因的潜在治疗药物。最终通过实时荧光定量PCR（RT-PCR）验证关键OSRGs的表达水平。

结果
本研究共鉴定出8个DE-OSRGs。基于3种模型的残差与均方根误差，随机森林模型表现最优，据此筛选出7个关键基因：TNFSF10、CBL、IFNG、CP、CASP8、APOA1及DDIT3。基因集富集分析（GSEA enrichment analysis）结果显示，TNFSF10、CP、DDIT3及IFNG（原文笔误为INFG）均富集于NOD样受体信号通路与自然杀伤细胞介导的细胞毒性通路。分子调控网络分析表明，TNFSF10与CBL均受非编码RNA调控。此外，本研究共预测出70种PCOS潜在治疗药物，其中布洛芬与DDIT3及IFNG相关。实时荧光定量PCR（RT-qPCR）验证结果证实，关键基因IFNG、DDIT3及APOA1的表达趋势与数据集结果一致，且差异具有统计学意义（P < 0.05）。

结论
本研究通过生物信息学分析鉴定出7个关键基因并构建了分子调控网络，对于探索PCOS的发病机制与治疗策略具有重要价值。