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皮肌炎差异表达基因鉴定及治疗药物预测

Identification of differentially expressed genes in dermatomyositis and prediction of therapeutic drugs

    摘要
  • 摘要:
    目的 探讨皮肌炎(DM)可能的发病机制、潜在治疗靶点及药物。
    方法 从基因表达综合(GEO)数据库下载符合筛选标准的健康人群和DM患者的芯片信息。利用R语言相关软件包筛选差异表达基因(DEGs); 分析DEGs的基因本体(GO)功能富集和京都基因与基因组百科全书(KEGG)通路富集情况。利用STRING在线数据库及Cytoscape软件构建蛋白质互作网络, 并筛选出关键基因加以验证。基于CIBERSORT算法分析免疫细胞浸润情况，分析核心基因表达量与免疫细胞丰度的相关性。利用DREIMT在线分析工具和Coremine Medical数据库预测潜在治疗DM疾病的药物。
    结果 与健康人群相比, DM患者肌肉组织中上调的DEGs为402个，下调的DEGs为150个，皮肤组织中上调的DEGs为686个，下调的DEGs为284个，肌肉和皮肤共有的DEGs为170个。GO、KEGG富集分析结果显示，上述DEGs主要富集于固有免疫应答、对病毒的防御反应、细胞质、细胞质膜等条目，富集于新型冠状病毒感染疾病、甲型流感、麻疹、丙型肝炎等通路。共筛选出10个核心基因，分别为STAT1、MX1、IFIT3、OAS2、IFI35、RSAD2、IFIT1、OAS1、ISG15和IRF7。22种免疫细胞浸润中，浆细胞浸润在肌肉组织中占主导地位，而M2巨噬细胞在皮肤组织中大量渗透。核心基因分析分别预测出排名前10的小分子化学药物和9种中药。
    结论 本研究利用生物信息学方法筛选的关键基因及药物可能在治疗DM疾病中具有重要作用。

     

    Abstract:
    Objective To investigate the possible pathogenesis, potential therapeutic targets and drugs of dermatomyositis (DM).
    Methods Chip information was downloaded from the Gene Expression Synthesis (GEO) database for healthy people and DM patients who met screening criteria. Differentially expressed genes (DEGs) were screened using R language related software packages; the gene ontology (GO) functional enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment of DEGs were analyzed. The protein interaction network was constructed using STRING online database and Cytoscape software, and key genes were screened and verified. CIBERSORT algorithm was used to analyze the infiltration of immune cells, and the correlation between the expression of hub genes and the abundance of immune cells was analyzed. The DREIMT online analysis tool and Coremine Medical database were used to predict potential drugs to treat DM diseases.
    Results Compared with healthy people, 402 DEGs were up-regulated and 150 DEGs were down-regulated in muscle tissue, 686 DEGs were up-regulated and 284 DEGs were down-regulated in skin tissue, and 170 DEGs were shared between muscle and skin. The results of GO and KEGG enrichment analysis showed that the above DEGs mainly enriched in the innate immune response, defense response to viruses, cytoplasm, plasma membrane and other items, and enriched in the novel coronavirus infection disease, influenza-A, measles, hepatitis C and other pathways. A total of 10 core genes were screened, which were STAT1, MX1, IFIT3, OAS2, IFI35, RSAD2, IFIT1, OAS1, ISG15 and IRF7. Among the 22 kinds of immune cell infiltration, plasma cell infiltration dominated in muscle tissue, while M2 macrophages penetrated in skin tissue in large numbers. Through the analysis of core genes, the top 10 small molecule chemical drugs and 9 traditional Chinese medicines were predicted.
    Conclusion In this study, the hub genes and drugs screened by bioinformatics method may play an important role in the treatment of DM.

     

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