摘要
目的:探索焦亡相关基因在多囊卵巢综合征(polycystic ovary syndrome,PCOS)发病机制中的作用,并构建PCOS的精确预测模型。方法:利用从基因表达公共数据库(Gene Expression Omnibus,GEO)中获取的3个微小RNA(microRNA,mRNA)表达谱,分析PCOS患者与正常健康女性之间焦亡相关基因(pyroptosis-related gene,PRG)的表达差异。采用广义线性模型(generalized linear model,GLM)、随机森林(random forest,RF)、支持向量机(support vector machine,SVM)和极限梯度提升(extreme gradient boosting,XGB)这4种机器学习算法来识别疾病特征基因。采用实时定量聚合酶链反应(real-time quantitative polymerase chain reaction,RT-qPCR)法检测10例PCOS患者和10例正常健康女性血浆中特征基因的表达量。结果:建立了基于PRG的PCOS预测模型和列线图。XGB显示出最高的准确性,决策曲线分析进一步支持了这一结果。一致聚类显示PCOS病例中有两个亚组,组2比组1表现出更多的免疫浸润。差异表达分析鉴定两个亚型之间的差异表达基因,并对基因进行富集分析。临床验证结果显示,含CARD结构域的凋亡相关斑点样蛋白(apoptosis associated speck like protein containing a CARD,又称PYD and CARD domain containing,PYCARD)、黑素瘤缺乏因子2(absent in melanoma 2,AIM2)、染色质修饰蛋白4B(chromatin modifying protein 4B,CHMP4B)和NOD样受体蛋白2(NOD-like receptor family pyrin domain containing 2,NLRP2)在PCOS患者组中的表达量明显高于正常对照组,差异有统计学意义,验证了基于PRG的PCOS预测模型的准确性。结论:本研究为PCOS与焦亡之间的关系提供了初步见解,并提出了PCOS的精确预测模型。
Objective:To explore the role of genes related to pyroptosis in the pathogenesis of polycystic ovary syndrome(PCOS)and construct an accurate prediction model for PCOS.Methods:The differential expression of pyroptosis-related genes(PRG)between PCOS patients and normal healthy women was analyzed by using three microRNA(mRNA)expression profiles obtained from the Gene Expression Omnibus(GEO)database.Four machine learning algorithms,namely the generalized linear model(GLM),random forest(RF),support vector machine(SVM),and extreme gradient boosting(XGB),were employed to identify the gene characteristics of PCOS.Real-time quantitative PCR(RT-qPCR)method was utilized to detect the expression levels of specific genes in the plasma of 10 PCOS patients and 10 normal healthy women.Results:A predictive model and a nomogram were established based on PRG to accurately predict PCOS.Among the four machine learning algorithms,the XGB method demonstrated the highest accuracy in validating the performance of the model,which was further supported by decision curve analysis.Consensus clustering revealed two distinct subgroups within PCOS cases,with Cluster 2 exhibiting higher level of immune infiltration compared with Cluster 1.Differential expression analysis was then conducted to identify differentially expressed genes between the two subtypes,followed by pathway enrichment analysis on the genes.Clinical verification showed that the plasma expression levels of the apoptosis associated speck like protein containing a CARD(also named PYD and CARD domain containing,PYCARD),the absent in melanoma 2(AIM2),the chromatin modif-yingprotein 4B(CHMP4B)and the NOD-like receptor family pyrin domain containing 2(NLRP2)were significantly higher in the PCOS patients than the healthy controls.This verifies the accuracy of the PCOS prediction model based on PRG.Conclusion:This study may offer preliminary insights into the correlation between PCOS and pyroptosis,and provide a precise predictive model for PCOS.
作者
陈讯
陈雯昕
张文
俞池园
许波群
CHEN Xun;CHEN Wenxin;ZHANG Wen;YU Chiyuan;XU Boqun(Department of Obstetrics and Gynecology,the Second Affiliated Hospital of Nanjing Medical University,Nanjing 210011;Department of Obstetrics and Gynecology,the Affiliated Sir Run Run Hospital of Nanjing Medical University,Nanjing 211100,China)
出处
《南京医科大学学报(自然科学版)》
北大核心
2025年第4期509-522,共14页
Journal of Nanjing Medical University(Natural Sciences)
基金
国家自然科学基金(81873820)
江苏省卫健委医学科研项目(H2023113)。
关键词
多囊卵巢综合征
细胞焦亡
免疫浸润
机器学习
生物信息学分析
polycystic ovary syndrome
pyroptosis
immune infiltration
machine learning
bioinformatics analysis
