多囊卵巢综合征中双硫死亡基因亚簇的鉴定和免疫特征分析  

Identification and immune characterization analysis of disulfide death gene subcluster inpolycystic ovary syndrome

作　　者：赵越梦 刘璨莹 郝健亨 王海军[1] 兰颖 冀来喜 ZHAO Yuemeng;LIU Liying;HAO Jianheng;WANG Haijun;LAN Ying;JI Laixi(Chengdu University of Traditional Chinese Medicine,Chengdu 610075,China;Shanxi University of Chinese Medicine,Jinzhong 030619,China;Hospital of Chengdu University of TCM,Chengdu 610072,China)

机构地区：[1]成都中医药大学,成都610075 [2]山西中医药大学,晋中030619 [3]成都中医药大学附属医院,成都610072

出　　处：《中华中医药杂志》2025年第3期1112-1117,共6页China Journal of Traditional Chinese Medicine and Pharmacy

基　　金：国家自然科学基金面上项目(No.82074549);国家自然科学基金青年科学基金项目(No.82105028);成都中医药大学“杏林学者”国家自然基金预研项目(No.ZRYY1912);成都中医药大学附属医院“百人计划”[No.院办发(2023)1号];成都中医药大学“杏林学者”青基人才专项(No.QJRC2022009)。

摘　　要：目的:深入研究多囊卵巢综合征(PCOS)患者中双硫死亡的潜在机制。方法:通过分析两个数据集(GSE34526和GSE137684),识别了与双硫死亡相关的差异表达基因,并进行了主成分分析和免疫浸润分析。运用加权基因共表达网络分析构建了基因网络,确定了PCOS的关键基因和分型基因,并筛选出核心基因。利用核心基因,通过机器学习算法筛选出了具有特征性的PCOS基因,并建立了临床预测模型。结果:在GSE80432数据集上建立的ROC曲线验证了模型的稳健性。发现3个与双硫死亡相关的差异表达基因,其中MYH9与免疫浸润密切相关。同时,鉴定了两个基因簇(Cluster 1和Cluster 2),其中Cluster1与免疫细胞的浸润密切相关。在机器学习模型中,随机森林树模型表现出卓越的预测能力(AUC=1.000)。此外,发现PCOS患者中FCN1与体重呈正相关(Cor=0.87,P=0.0047)。结论:本研究揭示了PCOS患者中双硫死亡的潜在机制,并提供了新的基因标志物和预测模型,为PCOS的诊断和治疗提供了新的思路。Objective:To investigate the potential mechanisms of disulfidptosis in patients with polycystic ovary syndrome(PCOS).Methods:Two datasets(GSE34526 and GSE137684)were analyzed to identify differentially expressed genes(DEGs)associated with disulfidptosis.Principal component analysis(PCA)and immune infiltration analysis were performed.Weighted gene co-expression network analysis(WGCNA)was used to construct a gene network,identify key genes and subtypespecific genes for PCOS,and screen for core genes.Based on the core genes,characteristic PCOS genes were selected using machine learning algorithms,and a clinical prediction model was established.Results:The robustness of the model was validated by the ROC curve on the GSE80432 dataset.We identified three DEGs associated with disulfidptosis,among which MYH9 was closely related to immune infiltration.Additionally,two gene clusters(Cluster 1 and Cluster 2)were identified,with Cluster 1 showing a strong corelation with immune cell infltration.In the machine learning models,the random forest model demonstrated excellent predictive performance(AUC=1.00O).Furthermore,FCN1 was found to be positively correlated with body weight in PCOS patients(Cor=0.87,P=0.0047).Conclusion:This study reveals the potential mechanisms of disulfidptosis in PCOS patients and provides novel gene markers and a predictive model,offering new insights for the diagnosis and treatment of PCOS.

关 键 词：多囊卵巢综合征 双硫死亡 颗粒细胞 免疫浸润 机器学习 生物信息分析 

分 类 号：R711.75[医药卫生—妇产科学]