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基于生物信息学分析MX1、IFI44和STAT1在狼疮性肾炎中的作用

陈春丽,  崔道林

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陈春丽, 崔道林. 基于生物信息学分析MX1、IFI44和STAT1在狼疮性肾炎中的作用[J]. 昆明医科大学学报.
引用本文: 陈春丽, 崔道林. 基于生物信息学分析MX1、IFI44和STAT1在狼疮性肾炎中的作用[J]. 昆明医科大学学报.
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Chunli CHEN, Daolin CUI. Bioinformatics-Based Analysis of the Roles of MX1，IFI44， and STAT1 in Lupus Nephritis[J]. Journal of Kunming Medical University.
Citation: Chunli CHEN, Daolin CUI. Bioinformatics-Based Analysis of the Roles of MX1，IFI44， and STAT1 in Lupus Nephritis[J]. Journal of Kunming Medical University.
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基于生物信息学分析MX1、IFI44和STAT1在狼疮性肾炎中的作用

  • 曲靖医学高等专科学校基础医学院，云南 曲靖　655000

基金项目: 云南省教育厅科学研究基金项目（2023J1760）
详细信息
    作者简介:

    陈春丽（1981～），女，山东郓城人，理学硕士，副教授，主要从事生物化学教学工作

    通讯作者:

    崔道林，E-mail：cdlynu2007@126.com

  • 中图分类号: R392.9

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

  • Dept. of Basic Medicine，Qujing Medical College，Qujing Yunnan 655000，China

    摘要
  • 摘要:   目的  本文旨在筛选与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。
    Abstract:   Objective  Lupus nephritis （LN） is a severe organ manifestation of systemic lupus erythematosus （SLE） and one of the leading causes of death in SLE patients. This study aims 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.
    • HTML全文

  • 图  1  整体研究设计

    Figure  1.  Overall research design

    下载: 全尺寸图片 幻灯片

    图  2  多基因芯片联合分析

    A：矫正前分析；B：矫正后分析。

    Figure  2.  Multi-gene chip combined analysis

    下载: 全尺寸图片 幻灯片

    图  3  构建WGCNA网络

    A：LN 共表达模块中 power 值筛选；B：模块特征基因与样本特征向量之间的皮尔逊相关系数；C：筛选 LN 的共同表达模块；D：黄绿模块和基因重要性之间的相关性，cor表示 基因显著性（gene significance，GS）和模块成员（module membership，MM）之间的绝对相关系数；E：蓝绿模块和基因重要性之间的相关性，cor表示 GS 和 MM 之间的绝对相关系数。

    Figure  3.  Construction of the WGCNA network

    下载: 全尺寸图片 幻灯片

    图  4  WGCNA 和 DEGs 的交集基因

    Figure  4.  Intersection genes of WGCNA and DEGs

    下载: 全尺寸图片 幻灯片

    图  5  筛选枢纽基因

    A：交集基因的蛋白质互作网络；B：由蛋白质互作网络分析确定的10个拓扑算法的UpSet图；C-D：使用SVM-RFE算法筛选SVM基因；E-F：从交集基因里筛选RandomForest基因及其重要性分析；G：韦恩图显示了3种方法获得的中枢基因的交集。

    Figure  5.  Screening of hub genes

    下载: 全尺寸图片 幻灯片

    图  6  验证枢纽基因

    A：IFI44、MX1和STAT1表达水平的ROC曲线；B：IFI44、MX1和STAT1在验证集中的相对表达量；***P < 0.001。

    Figure  6.  Validation of hub genes

    下载: 全尺寸图片 幻灯片

    表  1  实验数据集信息

    Table  1.   Experimental dataset information

    基因芯片注释平台LN患者/例正常人/例分组信息/组数据来源
    GSE22221GPL105581525训练Morris A C，et al[12]
    GSE112943GPL10558147训练Ko W C，et al[13]
    GSE99967GPL219702917训练Wither J E，et al[14]
    GSE32591GPL146636429训练Berthier C C，et al[15]
    GSE72798GPL5703010验证Ducreux J，et al[16]
    下载: 导出CSV

    表  2  正常组与LN患者差异基因

    Table  2.   Differential genes between normal group and LN patients

    差异基因基因
    上调基因IFI27，IFI44L，IFI44，MX1，LTF，RSAD2，CD163，HERC5，PLSCR1，OAS3，IFIT3，OAS1，OAS2，IFI6，IFIT1，LY96，MX2，HERC6，CREG1，SIGLEC1，EIF2AK2，CEACAM8，C1QB，IFITM1，IFIH1，IFIT2，DDX60，DYSF，ELANE，DEFA4，ABCA1，XAF1，IFITM3，TRIM22，ISG15，LCN2，RNASE6，OLFM4，APOBEC3A，IFI35，RNASE1，VSIG4，MMP9，DHX58，RNASE2，TOP2A，TLR2，LYN，HCK，HBD，MMP8，IRF7，ISG20，ZCCHC2，OLR1，CDC20，GBP2，MS4A4A，DDX58，CCL2，RRM2，SPATS2L，LHFPL2，GBP1，CTSG，CEACAM1，STAT1，CD38，ENTPD1，UBE2J1，TCN2，CXCL10，RTP4，FCER1G，TYMS，B4GALT5，CCNA2，RP2，C3AR1，SAMD9，YIPF6，CARHSP1，TAP1，IFI30，LGALS3BP，TRIM21，FAR2，STIL，PARP12，MFGE8，RNASE3，H2AFZ，CEACAM6，LAP3，YWHAH，GMPR，RIN2，SCARB2，LMNB1，MPO，LCP1，BPGM，TLR4，PRC1，LY6E，CFD，MS4A6A，TPX2，XK，LRRC32，SH3GLB1，TGM2， CTSS，OASL，COL1A2，GINS2，ANP32B，ADAR，RAPGEF5，LYZ，MARCKS，HMGB2，ATP6V1D，SP100，FAM46C，CAMP，COL4A1，TMEM140，ANXA3，FCN1，NETO2，MYOF，EVI2A，FUT4，DNAJC13，CD36，IFI16，POU2AF1，SAMSN1，TCN1，MR1，MNDA，GIMAP4，PRCP，NFE2，CA1，STAT2，PPP1R3D，TACC3，SLPI，C1QA，S100A8，NMI，TLR1，NUSAP1，HLX，ARG1，TMOD1，REXO2，PLA2G4A，PTEN，TNFSF10，NPL，PLEK，GINS3，NOD2，LXN，DNAJC15，HIST1H2AI，MFSD1，DEGS1，FAM46A，CENPN，ITGB2，SCO2，PSMC2，COL15A1，CDC123，DHRS9，HK3，SERPINB1，NOL7，ITGAM
    下调基因CLIC5，ASB1，PTGDS，EPHX2，CDKN1C，TNNC2，KIR2DL3，FCER1A，TGFBR3，FOSB
    下载: 导出CSV

    表  3  交集基因GO富集分析

    Table  3.   GO enrichment analysis of intersection genes

    ONTOLOGYIDDescriptionP
    BPGO:0051607defense response to virus0.000
    GO:0060337type I interferon signaling pathway0.000
    GO:0042742defense response to bacterium0.000
    GO:0019221cytokine-mediated signaling pathway0.000
    GO:0002221pattern recognition receptor signaling pathway0.000
    GO:0039529RIG-I signaling pathway0.000
    GO:0002446neutrophil mediated immunity0.000
    GO:0002709toll-like receptor signaling pathway0.000
    GO:0002709regulation of T cell mediated immunity0.002
    CCGO:0042581specific granule0.000
    GO:0034774secretory granule lumen0.000
    GO:0060205cytoplasmic vesicle lumen0.000
    GO:0005766primary lysosome0.000
    GO:0045335phagocytic vesicle0.001
    MFGO:0003725double-stranded RNA binding0.000
    GO:0008301DNA binding0.006
    GO:0004518nuclease activity0.008
    GO:0042379chemokine receptor binding0.009
    GO:0019207kinase regulator activity0.015
    下载: 导出CSV

    表  4  交集基因KEGG信号通路分析

    Table  4.   KEGG pathway analysis of intersection genes

    IDDescriptionP
    hsa05164Influenza A0.000
    hsa05171COVID-190.000
    hsa04621NOD-like receptor signaling pathway0.000
    hsa04622RIG-I-like receptor signaling pathway0.000
    hsa05150Staphylococcus aureus infection0.001
    hsa05322Systemic lupus erythematosus0.002
    hsa04657IL-17 signaling pathway0.004
    hsa04623Cytosolic DNA-sensing pathway0.016
    hsa04610Complement signaling pathway0.022
    hsa04217Necroptosis0.025
    hsa04620Toll-like receptor signaling pathway0.037
    hsa04613Neutrophil extracellular trap formation0.044
    下载: 导出CSV
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