Center for Reproductive Medicine, the Affiliated Qingyuan Hospital (Qingyuan People's Hospital), Guangzhou Medical University, Qingyuan, Guangdong, 511518, China.
Guangdong Engineering Technology Research Center of Urinary Continence and Reproductive Medicine, the Affiliated Qingyuan Hospital (Qingyuan People's Hospital), Guangzhou Medical University, Qingyuan, Guangdong, 511518, China.
Lipids Health Dis. 2024 Aug 15;23(1):248. doi: 10.1186/s12944-024-02237-3.
Changes in the oxidative stress and lipid metabolism (OSLM) pathways play important roles in polycystic ovarian syndrome (PCOS) pathogenesis and development. Consequently, a systematic analysis of genes related to OSLM was conducted to identify molecular clusters and explore new biomarkers that are helpful for the diagnostic of PCOS.
Gene expression and clinical data from 22 PCOS women and 14 normal women were obtained from the GEO database (GSE34526, GSE95728, and GSE106724). Consensus clustering identified OSLM-related molecular clusters, and WGCNA revealed co-expression patterns. The immune microenvironment was quantitatively assessed utilizing the CIBERSORT algorithm. Multiple machine learning models and connectivity map analyses were subsequently applied to explore potential biomarkers for PCOS, and nomograms were employed to develop a predictive multigene model of PCOS. Finally, the OSLM status of PCOS and the hub genes expression profiles were preliminarily verified using TUNEL, qRT‒PCR, western blot, and IHC assays in a PCOS mouse model.
19 differential expression genes (DEGs) related to OSLM were identified. Based on 19 DEGs that were strongly influenced by OSLM, PCOS patients were stratified into two distinct clusters, designated Cluster 1 and Cluster 2. Distinct differences in the immune cell proportions existed in normal and two PCOS clusters. The random forest showed the best results, with the least cross-entropy and the utmost AUC (cross-entropy: 0.111 AUC: 0.960). Among the 19 OSLM-related genes, CXCR1, ACP5, CEACAM3, S1PR4, and TCF7 were identified by a Bayesian network and had a good fit with PCOS disease risk by the nomogram (AUC: 0.990 CI: 0.968-1.000). TUNEL assays revealed more severe DNA damage within the ovarian granule cells of PCOS mice than in those of normal mice (P < 0.001). The RNA and protein expression levels of the five hub genes were significantly elevated in PCOS mice, which was consistent with the results of the bioinformatics analyses.
A novel predictive model was constructed for PCOS patients and five hub genes were identified as potential biomarkers to offer novel insights into clinical diagnostic strategies for PCOS.
氧化应激和脂质代谢（OSLM）途径的变化在多囊卵巢综合征（PCOS）的发病机制和发展中起着重要作用。因此，对与 OSLM 相关的基因进行了系统分析，以鉴定分子簇并探索有助于 PCOS 诊断的新生物标志物。
从 GEO 数据库（GSE34526、GSE95728 和 GSE106724）中获取了 22 名 PCOS 女性和 14 名正常女性的基因表达和临床数据。共识聚类确定了与 OSLM 相关的分子簇，WGCNA 揭示了共表达模式。使用 CIBERSORT 算法定量评估免疫微环境。随后应用多种机器学习模型和连接图分析来探索 PCOS 的潜在生物标志物，并使用列线图开发 PCOS 的多基因预测模型。最后，在 PCOS 小鼠模型中，使用 TUNEL、qRT-PCR、western blot 和 IHC 检测初步验证了 PCOS 的 OSLM 状态和枢纽基因表达谱。
确定了 19 个与 OSLM 相关的差异表达基因（DEGs）。基于受 OSLM 强烈影响的 19 个 DEGs，将 PCOS 患者分为两个不同的簇，分别命名为簇 1 和簇 2。正常组和两组 PCOS 簇的免疫细胞比例存在明显差异。随机森林的结果最好，交叉熵最小，AUC 最大（交叉熵：0.111 AUC：0.960）。在 19 个 OSLM 相关基因中，通过贝叶斯网络鉴定出 CXCR1、ACP5、CEACAM3、S1PR4 和 TCF7，并通过列线图预测与 PCOS 疾病风险具有良好的拟合度（AUC：0.990 CI：0.968-1.000）。TUNEL 检测显示，PCOS 小鼠卵巢颗粒细胞的 DNA 损伤比正常小鼠更严重（P<0.001）。在 PCOS 小鼠中，五个枢纽基因的 RNA 和蛋白表达水平均显著升高，与生物信息学分析结果一致。
构建了一种新的 PCOS 患者预测模型，并确定了五个枢纽基因作为潜在的生物标志物，为 PCOS 的临床诊断策略提供了新的见解。
