类风湿关节炎铁死亡特征基因CeRNA网络构建及免疫表现  

CeRNA interaction network and immune manifestation of ferroptosis-related signature genes in rheumatoid arthritis

作　　者：夏天[1] 李炳霖 肖发源 郑恩泽 陈跃平[2] Xia Tian;Li Binglin;Xiao Fayuan;Zheng Enze;Chen Yueping(Guangxi University of Chinese Medicine,Nanning 530000,Guangxi Zhuang Autonomous Region,China;Department of Orthopedic Trauma and Hand Surgery,Ruikang Hospital,Guangxi University of Chinese Medicine,Nanning 530000,Guangxi Zhuang Autonomous Region,China)

机构地区：[1]广西中医药大学,广西壮族自治区南宁市530000 [2]广西中医药大学附属瑞康医院创伤骨科与手外科,广西壮族自治区南宁市530000

出　　处：《中国组织工程研究》2024年第16期2561-2567,共7页Chinese Journal of Tissue Engineering Research

基　　金：国家自然科学基金资助项目(81960803),项目负责人:陈跃平;广西中医药大学自治区级博士研究生科研创新项目(YCBXJ2021019),项目负责人:夏天;广西中医药适宜技术开发与推广项目(GZSY22-39),项目负责人:夏天。

摘　　要：背景:研究发现铁死亡相关基因在类风湿关节炎的发病机制中占据重要地位,但目前尚缺乏关于类风湿关节炎铁死亡特征基因的免疫表现及CeRNA互作网络的构建,而机器学习作为生物信息学中强大的特征基因选择算法能更精确地筛选出在类风湿关节炎发病机制中占主导地位的铁死亡特征基因。目的:利用生物信息学与机器学习方法筛选类风湿关节炎铁死亡特征基因,并分析铁死亡特征基因与免疫浸润的相关性及铁死亡特征基因CeRNA的网络构建。方法:从GEO数据库获取与类风湿关节炎相关的芯片,利用R语言提取铁死亡相关基因及其差异基因表达;使用机器学习方法对差异基因进行筛选,即运用LASSO回归与SVM-RFE方法进行特征基因筛选,对两者过滤后的基因进行再次交集,最终得到类风湿关节炎的特征基因,运用ROC曲线评估筛选后的疾病特征基因诊断疾病的准确性;利用CIBERSORT算法分析类风湿关节炎与正常滑膜组织的免疫浸润情况,并分析铁死亡特征基因与免疫细胞的相关性,最后构建类风湿关节炎铁死亡疾病特征基因的CeRNA网络并对疾病特征基因进行验证。结果与结论:①得到与类风湿关节炎相关铁死亡基因150个,其中55个上调基因,95个下调基因;②GO与KEGG富集分析分别得到18个GO显著相关条目与30个KEGG条目,主要涉及金属离子稳态、有铁离子稳态与氧化应激反应等;③机器学习分析最终获得疾病特征基因GABARAPL1、SAT1;④GSEA分析发现脂肪细胞因子信号通路、药物代谢细胞色素P450、脂肪酸代谢、PPAR信号通路、酪氨酸代谢主要集中在GABARAPL1高表达时,趋化因子信号通路、肠道免疫网络对IGA产生的影响主要集中在SAT1高表达时;⑤免疫浸润分析发现类风湿关节炎与9种免疫细胞与正常组织存在明显差异,其中浆细胞、T细胞CD8、T细胞滤泡辅助器在疾病组中为高表达状态,其余�BACKGROUND:Ferroptosis-related genes have been found to play an important role in the pathogenesis of rheumatoid arthritis.However,there is currently a lack of immune expression of ferroptosis-related signature genes in rheumatoid arthritis and the construction of competing endogenous RNA(CeRNA)interaction networks.Machine learning,as a powerful signature gene selection algorithm based on bioinformatics,can more accurately identify ferroptosisrelated signature genes that dominate the pathogenesis of rheumatoid arthritis.OBJECTIVE:To screen ferroptosis-related signature genes in rheumatoid arthritis using bioinformatics and machine learning methods,and to analyze the correlation between ferroptosis-related signature genes and immune infiltration and the construction of CeRNA network of ferroptosis-related signature genes.METHODS:Rheumatoid arthritis-related microarrays were obtained from the GEO database,and ferroptosis-related genes and their differential gene expression were extracted using R language.The differentially expressed genes were screened using machine learning methods.The LASSO regression and SVM-RFE methods were used for signature gene screening,and the genes filtered by both were re-intersected to finally obtain the signature genes in rheumatoid arthritis.Receiver operating characteristic curves were used to assess the accuracy of the screened signature genes for disease diagnosis.Immune infiltration of rheumatoid arthritis and normal synovial tissues was analyzed using the CIBERSORT algorithm,and the correlation between the signature genes and immune cells was analyzed.Finally,the CeRNA network of ferroptosis-related signature genes for rheumatoid arthritis was constructed and the disease signature genes were validated.RESULTS AND CONCLUSION:A total of 150 ferroptosis-related genes in rheumatoid arthritis were obtained,including 55 up-regulated genes and 95 downregulated genes.GO and KEGG enrichment analyses identified 18 GO significantly correlated entries and 30 KEGG entries respectively,mainly

关 键 词：类风湿关节炎 生物信息学 机器学习 铁死亡 CeRNA网络构建 

分 类 号：R459.9[医药卫生—治疗学] R318[医药卫生—临床医学] R593.22