WGCNA和机器学习识别骨关节炎铁死亡特征基因及实验验证  

Identification of ferroptosis signature genes in osteoarthritis based on WGCNA and machine learning and experimental validation

作　　者：徐文飞 明春玉 段戡[1] 袁长深[1] 郭锦荣[1] 胡琪 曾超[1] 梅其杰[1] Xu Wenfei;Ming Chunyu;Duan Kan;Yuan Changshen;Guo Jinrong;Hu Qi;Zeng Chao;Mei Qijie(Orthopedic Department of the Limbs,First Affiliated Hospital of Guangxi University of Chinese Medicine,Nanning 530023,Guangxi Zhuang Autonomous Region,China;Department of Geriatrics,Ruikang Affiliated Hospital of Guangxi University of Chinese Medicine,Nanning 530011,Guangxi Zhuang Autonomous Region,China)

机构地区：[1]广西中医药大学第一附属医院四肢骨伤科,广西壮族自治区南宁市530023 [2]广西中医药大学附属瑞康医院老年病科,广西壮族自治区南宁市530011

出　　处：《中国组织工程研究》2024年第30期4909-4914,共6页Chinese Journal of Tissue Engineering Research

基　　金：国家自然科学基金(82160912),项目负责人:段戡;国家自然科学基金(82060875),项目负责人:袁长深;2023年广西中医药大学第一附属医院青年科学基金项目(院字[2023]29号),项目负责人:徐文飞。

摘　　要：背景:铁死亡与骨关节炎发生、发展密切相关,但具体特征基因及调控机制尚不清楚。目的:运用WGCNA及多种机器学习方法识别骨关节炎铁死亡特征基因及免疫浸润分析。方法:从GEO数据库下载骨关节炎相关数据集,同时在FerrDb网站中获取铁死亡相关基因,采用R语言对骨关节炎数据集进行批次校正、提取骨关节炎铁死亡基因并进行差异分析,对差异基因进行GO功能及KEGG信号通路分析;同时运用WGCNA分析及机器学习(随机森林、LASSO回归及SVM-RFE分析)筛选骨关节炎铁死亡特征基因,并进行体外细胞实验,将软骨细胞分为正常组和骨关节炎组,运用数据集及qPCR验证表达并行相关免疫浸润分析。结果与结论:①经批次校正及PCA分析获得骨关节炎基因12548个,同时获得铁死亡基因484个,进而得到24个骨关节炎铁死亡差异基因;②GO分析主要涉及对氧化应激反应、对有机磷反应等生物过程;涉及细胞顶端、顶端质膜等细胞组分;涉及血红素结合、四吡咯结合等分子功能;③KEGG分析显示,骨关节炎铁死亡差异基因与白细胞介素17信号通路、肿瘤坏因子信号通路等信号通路有关;④运用WGCNA分析及机器学习筛选后获得特征基因KLF2;通过基因芯片验证后发现实验组半月板组织中KLF2基因表达高于对照组(P=0.00014);⑤体外细胞实验显示,骨关节炎组软骨细胞中Ⅱ型胶原、KLF2基因表达低于对照组(P<0.05),同时在骨关节炎铁死亡中肥大细胞与树突状细胞密切相关(r=0.99),KLF2与自然杀伤细胞(r=-1,P=0.017)、滤泡辅助性T细胞(r=-1,P=0.017)等密切相关;⑥结果显示,运用WGCNA分析及机器学习方法证实KLF2可作为骨关节炎铁死亡的特征基因,可能通过干预KLF2来改善骨关节炎铁死亡。BACKGROUND:Ferroptosis is strongly associated with the occurrence and progression of osteoarthritis,but the specific characteristic genes and regulatory mechanisms are not known.OBJECTIVE:To identify osteoarthritis ferroptosis signature genes and immune infiltration analysis using the WGCNA and various machine learning methods.METHODS:The osteoarthritis dataset was downloaded from the GEO database and ferroptosis-related genes were obtained from the FerrDb website.R language was used to batch correct the osteoarthritis dataset,extract osteoarthritis ferroptosis genes and perform differential analysis,analyze differentially expressed genes for GO function and KEGG signaling pathway.WGCNA analysis and machine learning(random forest,LASSO regression,and SVM-RFE analysis)were also used to screen osteoarthritis ferroptosis signature genes.The in vitro cell experiments were performed to divide chondrocytes into normal and osteoarthritis model groups.The dataset and qPCR were used to verify expression and correlate immune infiltration analysis.RESULTS AND CONCLUSION:(1)12548 osteoarthritis genes were obtained by batch correction and PCA analysis,while 484 ferroptosis genes were obtained,resulting in 24 differentially expressed genes of osteoarthritis ferroptosis.(2)GO analysis mainly involved biological processes such as response to oxidative stress and response to organophosphorus,cellular components such as apical and apical plasma membranes,and molecular functions such as heme binding and tetrapyrrole binding.(3)KEGG analysis exhibited that differentially expressed genes of osteoarthritis ferroptosis were related to signaling pathways such as the interleukin 17 signaling pathway and tumor necrosis factor signaling pathway.(4)After using WGCNA analysis and machine learning screening,we obtained the characteristic gene KLF2.After validation by gene microarray,we found that the gene expression of KLF2 was higher in the test group than in the control group in the meniscus(P=0.00014).(5)In vitro chondrocyte assay showed t

关 键 词：WGCNA分析 机器学习 骨关节炎 铁死亡 特征基因 免疫浸润分析 体外细胞实验 

分 类 号：R459.9[医药卫生—治疗学] R364[医药卫生—临床医学] R684.3