通过生物信息学分析肾移植后慢性排斥反应差异表达基因

To analyze the differentially expressed genes in chronic rejection after renal transplantationby bioinformatics

目的:通过利用生物信息学技术分析肾移植后慢性排斥反应的差异表达基因,可以筛选出与该疾病发展相关的潜在致病靶点,为寻找新的治疗靶点提供了理论依据。方法:从基因表达谱综合数据库下载基因微阵列数据,并进行交叉计算以确定差异表达基因(DEGs)。将DEGs与基因本体(GO)分析是用来研究基因在不同条件下的表达差异以及其功能和相互关系的方法,而京都基因和基因组百科全书(KEGG)富集分析则是用来探索基因在特定生物过程中的功能和通路的工具。通过对免疫细胞浸润的分布进行计算,可以将排斥组的免疫浸润结果作为性状,在加权基因共表达网络分析(WGCNA)中进行分析,以获得与排斥相关的基因。然后,利用STRING数据库和Cytoscape软件构建蛋白质-蛋白质相互作用网络(PPI),以识别枢纽基因标记。结果:从3个数据集(GSE7392、GSE181757、GSE222889)共获得60个整合后的DEGs。通过GO及KEGG分析,GEDs主要集中在免疫应答的调节、防御反应、免疫系统过程的调节、刺激反应等。通路主要富集在抗原处理和呈递、EB病毒感染、移植物抗宿主、同种异体移植排斥、自然杀伤细胞介导的细胞毒性等。再利用WGCNA和PPI网络筛选后,HLA-A、HLA-B、HLA-F、TYROBP被鉴定为枢纽基因(Hub基因)。选择带有临床信息的数据GSE21374构建4个枢纽基因的诊断效能及风险预测模型图,结果认为4个Hub基因均具有良好诊断价值(曲线下面积在0.794-0.819)。从推理上可以得出结论,HLA-A、HLA-B、HLA-F和TYROBP这4种基因可能在肾移植后慢性排斥反应的发生和进展中具有重要作用。结论:DEGs在研究肾移植后慢性排斥反应的发病机制中起到重要作用,可以通过富集分析和枢纽基因筛选,以及相关诊断效能和疾病风险预测的推断分析,为进一步研究肾移植后慢性排斥反应的发病机制和发现新的治疗靶点提供理论支持。

Objective:To use bioinformatics technology to analyze the differentially expressed genes in chronic rejection after renal transplantation,we can screen potential pathogenic targets related to the development of the disease,and provide theoretical basis for finding new therapeutic targets.Methods:Gene microarray data were downloaded from the Gene Expression Omnibus(GEO)database,and cross-calculations were performed to identify differentially expressed genes(DEGs).Differentially expressed genes(DEGs)and gene ontology(GO)analysis are used to study the expression differences of genes under different conditions as well as their functions and interrelationships,while Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis is a tool to explore the functions and pathways of genes in specific biological processes.By calculating the distribution of immune cell infiltration,the immune infiltration results of the rejection group can be analyzed as traits in the weighted gene co-expression network analysis(WGCNA)to obtain the genes related to rejection.Then,a protein-protein interaction network(PPI)was constructed using STRING database and Cytoscape software to identify hub gene markers.Results:A total of 60 integrated DEGs were obtained from 3 datasets(GSE7392,GSE181757,GSE222889).Through GO and KEGG analysis,GEDs mainly focused on the regulation of immune response,defense response,regulation of immune system processes,and stimulus response.Pathways were mainly enriched in antigen processing and presentation,Epstein-Barr virus infection,graft-versus-host disease,allograft rejection,natural killer cell-mediated cytotoxicity,etc.HLA⁃A,HLA⁃B,HLA⁃F and TYROBP were identified as Hub genes by WGCNA and PPI network screening.The data GSE21374 with clinical information was selected to construct the diagnostic efficacy and risk prediction model maps of the four Hub genes,and the results showed that all the four hub genes had good diagnostic value(the area under the curve was 0.794-0.819).It can be concluded by reasoning that four genes,HLA⁃A,HLA⁃B,HLA⁃F and TYROBP,may have important roles in the development and progression of chronic rejection after renal transplantation.Conclusion:DEGs play an important role in the study of the pathogenesis of chronic rejection after kidney transplantation.Through enrichment analysis,hub gene screening,and inference analysis of related diagnostic efficacy and disease risk prediction,it provides theoretical support for further study of the pathogenesis of chronic rejection after kidney transplantation and discovery of new therapeutic targets.