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OA

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.