基于基因表达谱的银屑病自噬相关关键基因筛选和候选药物预测

Screening of autophagy-related key genes in psoriasis and candidate drugs prediction based on gene expression profiling

目的利用生物信息学方法探究银屑病自噬相关关键基因(Hub)和潜在治疗药物。方法通过基因表达谱数据集筛选差异表达基因(DEGs),收集自噬相关基因(ARGs)并与DEGs取交集得到自噬相关DEGs(DEARGs),构建蛋白质-蛋白质作用网络(PPI)进行基因本体功能注释,京都基因与基因组百科全书通路富集分析,应用分子复杂检测(MCODE)算法插件筛选显著的功能模块及Hub,并经独立数据集验证,然后运用DGIdb数据库预测潜在治疗药物。结果筛选得到39个DEARGs,DEARGs在白细胞介素-17(IL-17)调节、炎性反应调控、自噬等方面显著富集,DEARGs主要参与腺苷酸活化蛋白激酶、脂肪细胞因子、核苷酸寡聚化结构域样受体、胰岛素抵抗等通路。PPI鉴定出6个Hub,即血管紧张素Ⅱ受体1、过氧化物酶体增生激活受体γ、瘦素、脂联素、烟酰胺磷酸核糖转移酶和IL-6,受试者工作特征曲线和数据集表达分析证实了6个中枢基因与银屑病的相关性。预测了包括环孢素、阿达木单抗、利妥昔单抗、异环磷酰胺在内的77种已批准药物可作为银屑病的潜在治疗药物。结论采用生物信息学方法鉴定Hub预测潜在治疗药物有助于了解银屑病的分子机制,为其诊治提供新的见解。

Objective To explore the autophagy-related key genes(Hub)for psoriasis and potential therapeutic agents by bioinformatics method.Methods The differentially expressed genes(DEGs)were screened through expression profile data set.Autophagy related genes(ARGs)were collected and intersected with DEGs to obtain autophagy related differentially expressed genes(DEARGs).Then protein-protein interaction network(PPI)of DEARGs was constructed,and the Gene ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis were performed.Significant functional modules and Hub were screened by MCODE plug-in,and verified by independent data set.Then the DGIdb database was used to predict the potential therapeutic drugs.Results A total of 39 DEARGs were obtained by screening.DEARGs were significantly enriched in the aspects of IL-17 regulation,inflammatory response regulation,autophagy,etc.DEARGs mainly participated in AMPK,Adipocytokine,NOD-like receptors and insulin resistance signalling pathways.Six Hub genes,i.e.,AGTR1,PPARG,LEP,ADIPOQ,NAMPT and IL-6,were identified by PPI,the correlation between 6 Hub genes and psoriasis was confirmed by the ROC curves and data set expression analysis.A total of 77 approved drugs,including ciclosporin,adalimumab,rituximab and ifosfamide were predicted to be potential therapeutic drugs for psoriasis.Conclusion Adopting the bioinformatics methods to identify Hub and predict potential therapeutic drugs is conducive to understand the molecular mechanisms of psoriasis and provide the new insights for diagnosis and treatment of psoriasis.