Construction of a diagnostic model for polycystic ovary syndrome based on necroptosis-related genes and drug screening

doi:10.3760/cma.j.cn441417-20241209-10002

Abstract

Abstract:

Objective　To collect and analyze the gene expression data of granulosa cells from the polycystic ovary syndrome (PCOS) samples in the GEO database, to construct a PCOS diagnostic model based on necroptosis-related genes, and to further screen potential therapeutic drugs. Methods　Five algorithms, including Lasso, support vector machine (SVM), random forest (RF), gradient boosting algorithm (XGB), and generalized linear model (GLM), were used to select the necroptosis-related feature genes associated with PCOS, and a diagnostic nomogram model was constructed. The model was validated using external datasets. Simultaneously, the sensitive drugs related to the feature genes were screened through the DGIdb database. t test, one-way analysis of variate, and Kruskal-Wallis rank sum test were used for the statistical comparisons. The correlation was analyzed by the Spearman correlation analysis. Results　Two downregulated genes (PANX1 and HSPA4) and three upregulated genes (STAT5B, PYGM, and TRIM11) were identified in the PCOS samples. The nomogram model based on these genes demonstrated an area under the receiver operating characteristic curve of this model was 0.875 in the validation set, indicating good diagnostic reliability. A total of 34 approved drugs related to the feature genes were identified. Conclusions　The PCOS diagnostic model constructed based on the necroptosis-related genes (PANX1, STAT5B, HSPA4, PYGM, and TRIM11) shows good predictive performance. The 34 clinically available drugs identified through the DGIdb database provide new insights for the diagnosis and treatment of PCOS.

Key words: Polycystic ovary syndrome, , Necroptosis, , Diagnostic biomarkers, , Drug therapy, 　Diagnostic model

摘要：

目的　本研究旨在通过Gene expression Omnibus（GEO）数据库收集并分析多囊卵巢综合征（PCOS）患者样本的颗粒细胞基因表达数据，构建基于坏死性凋亡相关基因的PCOS诊断模型，并进一步筛选潜在的治疗药物。方法　采用Lasso、支持向量机（SVM）、随机森林（RF）、梯度提升算法（XGB）和广义线性模型（GLM）5种算法，筛选出与PCOS相关的坏死性凋亡特征基因，构建PCOS诊断列线图模型，并使用外部数据集进行验证。同时，通过DGIdb数据库筛选与特征基因相关的敏感药物。采用t检验、单因素方差分析（ANOVA）、Kruskal-Wallis秩和检验进行统计比较，采用Spearman相关性分析。结果　在PCOS样本中筛选到2个下调基因（PANX1、HSPA4）及3个上调基因（STAT5B、PYGM、TRIM11）。基于筛选结果构建的列线图模型在验证集中受试者操作特征曲线（ROC）的曲线下面积达0.875，显示出良好的诊断可靠性。最终筛选出34种已获批准上市的特征基因相关敏感药物。结论　基于坏死性凋亡相关基因PANX1、STAT5B、HSPA4、PYGM和TRIM11构建的PCOS诊断模型具有良好的预测性能。通过DGIdb数据库筛选出34种可供临床选择的药物，为PCOS诊断与治疗提供了新的思路。

关键词: 多囊卵巢综合征, , , 坏死性凋亡, , , 诊断性生物标志物, , , 药物治疗, , , 诊断模型

Zhang Huiping, Li Jiao, Ji Kaijie, Zhang Yuanyuan.

Construction of a diagnostic model for polycystic ovary syndrome based on necroptosis-related genes and drug screening [J]. International Medicine and Health Guidance News, 2025, 31(10): 1591-1598.

张慧萍李姣计凯杰张媛媛. 基于坏死性凋亡相关基因构建多囊卵巢综合征的诊断模型及药物筛选 [J]. 国际医药卫生导报, 2025, 31(10): 1591-1598.

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