基于生物信息学分析MX1、IFI44和STAT1在狼疮性肾炎中的作用  

Bioinformatics-Based Analysis of the Roles of MX1,IFI44,and STAT1 in Lupus Nephritis

作　　者：崔道林 陈春丽 周正宏 龚蕾 CUI Daolin;CHEN Chunli;ZHOU Zhenghong;GONG Lei(School of Basic Medical Sciences,Qujing Medical College,Qujing Yunnan 655100,China;School of Medical Technology,Qujing Medical College,Qujing Yunnan 655100,China)

机构地区：[1]曲靖医学高等专科学校基础医学院,云南曲靖655100 [2]曲靖医学高等专科学校医学技术学院,云南曲靖655100

出　　处：《昆明医科大学学报》2024年第12期105-114,共10页Journal of Kunming Medical University

基　　金：云南省教育厅科学研究基金资助项目(2023J1760);曲靖医学高等专科学校大学生科技创新资助项目(2024DZ004)。

摘　　要：目的旨在筛选与LN相关的潜在生物标志物,以期用于早期诊断、病情监测和更精准的治疗方案制定。方法从基因表达谱数据库(gene expression omnibus,GEO)下载了GSE22221、GSE112943、GSE99967和GSE32591的基因表达数据。通过加权基因共表达网络分析(weighted gene co-expression network analysis,WGCNA)和微阵列数据的线性模型(linear models for microarray data,LIMMA),获得了交集基因。随后,利用基因本体论(gene ontology,GO)和京都基因与基因组百科全书(kyoto encyclopedia of genes and genomes,KEGG)对这些交集基因进行了生物功能和信号通路分析。接着,通过蛋白质-蛋白质相互作用(protein-protein interaction,PPI)网络分析、CytoHubba算法、支持向量机(support vector machine,SVM)和随机森林(random forest,RF)方法,筛选出了与LN高度相关的枢纽基因,进行了受试者工作特征曲线(receiver operating characteristic,ROC)分析,并利用GSE72798数据集对3个潜在的生物标志物进行了验证。结果WGCNA分析获得绿黄色模块(P=7.4e−40)和青色模块(P=1.5e−14);利用LIMMA法筛选到193个差异表达基因;共鉴定出113个LN相关的交集基因,GO和KEGG分析显示这些基因主要富集在病毒或细菌的防御、I型干扰素信号途径、中性粒细胞介导的免疫和Toll样受体信号等方面。通过CytoHubba、SVM和RF 3种方法筛选出MX1、IFI44和STAT1,其曲线下的面积(area under the curve,AUC)分别为0.874、0.879和0.833。验证数据集显示,MX1、IFI44和STAT1在LN患者中的表达显著高于健康人群(P<0.001)。结论MX1、IFI44和STAT1在LN的发病机制中起到了关键作用,可能成为LN的重要生物标志物和未来的潜在治疗靶点。Objective To identify potential biomarkers associated with LN,with the goal of improving early diagnosis,disease monitoring,and the development of more precise treatment strategies.Methods Gene expression data were downloaded from the Gene Expression Omnibus(GEO)database for datasets GSE22221,GSE112943,GSE99967,and GSE32591.Intersecting genes were obtained through the application of weighted gene co-expression network analysis(WGCNA)and linear models for microarray data(LIMMA).Subsequently,biological function and pathway analyses were conducted on these intersecting genes using Gene Ontology(GO)and the Kyoto Encyclopedia of Genes and Genomes(KEGG).Next,protein-protein interaction(PPI)network analysis was performed,and hub genes highly associated with LN were identified using the CytoHubba algorithm,support vector machine(SVM),and random forest(RF)methods.Receiver operating characteristic(ROC)analysis was performed,and three potential biomarkers were validated using the GSE72798 dataset.Results The green-yellow module(P=7.4e−40)and the cyan module(P=1.5e−14)were identified through WGCNA analysis.A total of 193 differentially expressed genes were identified using LIMMA,with 113 intersecting genes related to LN being identified.GO and KEGG analyses indicated that these genes were mainly enriched in viral or bacterial defense,type I interferon signaling pathway,neutrophil-mediated immunity,and Toll-like receptor signaling.MX1,IFI44,and STAT1 were identified as hub genes using CytoHubba,SVM,and RF methods,with AUC values of 0.874,0.879,and 0.833,respectively.Validation using the GSE72798 dataset demonstrated that the expression of MX1,IFI44,and STAT1 was significantly higher in LN patients compared to healthy individuals(P<0.001 for all).Conclusion MX1,IFI44,and STAT1 play crucial roles in the pathogenesis of LN and may serve as important biomarkers and potential therapeutic targets for LN.

关 键 词：狼疮肾炎 系统性红斑狼疮 生物标志物 枢纽基因 I型干扰素 

分 类 号：R392.9[医药卫生—免疫学]