江汉大学学报(自然科学版) ›› 2024, Vol. 52 ›› Issue (2): 46-55.doi: 10.16389/j.cnki.cn42-1737/n.2024.02.006

• 医学 • 上一篇    

基于生物信息学方法筛选特应性皮炎关键基因和相关药物预测

王晓晨1,陈 露1,刘 畅1,陈晓青1,李 芃2,邱文洪*1   

  1. 1. 江汉大学 医学部,湖北 武汉 430056;2. 武汉市中心医院 皮肤科,湖北 武汉 430014
  • 发布日期:2024-04-11
  • 通讯作者: 邱文洪
  • 作者简介:王晓晨(1995— ),女,硕士生,研究方向:自身免疫病的发病机制与干预策略。

Screening Key Genes of Atopic Dermatitis and Prediction of Related Drugs Based on Bioinformatics Method

WANG Xiaochen1,CHEN Lu1,LIU Chang1,CHEN Xiaoqing1,LI Peng2,QIU Wenhong*1   

  1. 1. School of Medicine,Jianghan University,Wuhan 430056,Hubei,China;2. Department of Dermatology,The Central Hospital of Wuhan,Wuhan 430014,Hubei,China
  • Published:2024-04-11
  • Contact: QIU Wenhong

摘要: 目 的 筛选特应性皮炎皮损区的关键基因及对潜在的治疗药物的预测。方 法 从 GEO 数据库中获得 GSE193309 高通量测序数据,运用 R 语言进行差异表达基因筛选、GO 功能富 集分析和 KEGG 通路富集分析,并通过 String 网站进行蛋白互作网络(PPI)的构建;用 Cytoscape 软件中的插件 MCODE 进行模块分析,筛选出特应性皮炎皮损区的关键基因;基于 CIBERSORT 算法,对特应性皮炎皮损区与非皮损区的皮肤之间的免疫细胞进行差异分析,最后利用 Connectivity Map 预测可以减轻特应性皮炎皮损症状的潜在小分子化合物。结 果 本研究共筛选出 1 847 个 差 异 表 达 基 因 和 11 个 关 键 基 因 PI3、SPRR2B、LCE3C、LCE3E、SPRR1A、LCE3A、SPRR2A、 SPRR2F、SPRR1B、LCE3D 和 LCE5A。GO 分析共富集 962 个功能,包括免疫系统过程、白细胞 的激活、防御反应等;KEGG 分析共富集 64 条通路,差异表达基因与细胞因子-细胞因子受体相互 作用最相关。未活化的树突状细胞、M2 巨噬细胞和未活化的肥大细胞在表皮免疫微环境中占比 最高。预测出小分子化合物埃博霉素、苯甲酰喹、茚地那韦、KU-0063794、PI-103、头孢雷特、 氨 氯 地 平 、PI-828 可 作 为 减 轻 特 应 性 皮 炎 局 部 皮 损 的 潜 在 药 物 。 结 论 PI3、SPRR2B、 LCE3C、LCE3E、SPRR1A、LCE3A、SPRR2A、SPRR2F、SPRR1B、LCE3D 和 LCE5A 可能是特应 性皮炎皮损发生相关的关键基因,预测的 8 个小分子化合物可为后续的药物研发提供理论参考。

关键词: 特应性皮炎, 生物信息学, 免疫浸润, 潜在治疗药物

Abstract: Objective To screen the key genes in the lesion area of atopic dermatitis and predict potential therapeutic drugs. Methods The GSE193309 high- throughput sequencing data was obtained from the GEO database. The R language was used for differentially expressed gene screening,GO function enrichment analysis and KEGG pathway enrichment analysis,and the protein-protein interaction (PPI) networks were constructed via the String website. Module analysis was performed using the MCODE plugin of Cytoscape software to screen key genes in AD skin lesions. Based on the CIBERSORT algorithm,the differences in immune cells between the damaged and non-damaged skin of AD were analyzed. Finally,the Connectivity Map was used to predict the potential small molecule compounds that could alleviate the symptoms of AD lesions. Results A total of 1 847 differentially expressed genes and 11 key genes PI3, SPRR2B, LCE3C, LCE3E, SPRR1A,LCE3A,SPRR2A,SPRR2F,SPRR1B,LCE3D and LCE5A were screened out. A total of 962 functions were enriched by GO analysis,including the immune system process,leukocyte activation,defense response,and so on. A total of 64 signaling pathways were enriched by KEGG analysis,and the differentially expressed genes were most closely related to cytokine-cytokine receptor interaction. Resting dendritic cells,macrophagesM2,and resting mast cells accounted for the highest proportion in the epidermal immune microenvironment. Small molecule compounds such as epirubicin,benzoylquine,indinavir, KU-0063794,PI-103,ceforanide,amlodipine,and PI-828 were predicted as potential drugs to alleviate local skin lesions of AD. Conclusion PI3,SPRR2B,LCE3C,LCE3E, SPRR1A,LCE3A,SPRR2A,SPRR2F,SPRR1B,LCE3D,and LCE5A could be the key genes associated with the occurrence of atopic dermatitis,and the eight predicted small molecule compounds may provide a theoretical reference for subsequent drug development.

Key words: atopic dermatitis, bioinformatics, immune infiltration, potential therapeutic agent

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