江汉大学学报（自然科学版）2024，Vol.52Issue(2)：46-55,10.DOI:10.16389/j.cnki.cn42-1737/n.2024.02.006
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
Screening Key Genes of Atopic Dermatitis and Prediction of Related Drugs Based on Bioinformatics Method
摘要
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 1847 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,macrophages-M2,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.关键词
特应性皮炎/生物信息学/免疫浸润/潜在治疗药物Key words
atopic dermatitis/bioinformatics/immune infiltration/potential therapeutic agent分类
医药卫生引用本文复制引用
王晓晨,陈露,刘畅,陈晓青,李芃,邱文洪..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[J].江汉大学学报（自然科学版）,2024,52(2):46-55,10.基金项目
国家自然科学基金资助项目(31671092) （31671092）
教育部高等教育司产学合作教育资助项目((202102585001) （(202102585001）
湖北省教育厅资助项目(2020435) （2020435）
