基于生物信息学和机器学习鉴定2型糖尿病肾病肾小管间质损伤相关基因

Identification of genes related to tubulointerstitial injury in type 2 diabetic nephropathy based on bioinformatics and machine learning

目的:应用生物信息学多芯片联合分析方法和机器学习技术探索糖尿病肾病(DN)肾小管间质损伤相关基因并阐释其潜在作用机理,为DN诊治提供新思路。方法:从GEO数据库检索获取4个DN肾小管间质组织基因表达数据集,其中GSE30122、GSE47185和GSE99340作为联合芯片数据集,而GSE104954作为独立验证数据集。使用R语言鉴定差异表达基因(DEGs),并进行基因本论(GO)富集和KEGG通路富集分析、基因集富集分析、免疫细胞浸润分析。进一步利用LASSO回归、SVM-RFE和RF机器学习算法筛选核心基因,同时进行外部验证和受试者工作曲线分析,并构建列线图预测模型,最后借助Nephroseq数据库探索其对DN患者临床特征的影响。结果:共获取107个DEGs,富集分析发现DN肾小管间质损伤主要涉及适应性免疫应答、淋巴细胞介导免疫、免疫效应器过程调节等免疫相关功能与金黄色葡萄球菌感染、补体和凝血级联、吞噬体、Th1和Th2细胞分化等免疫炎症相关通路,此外细胞黏附分子、细胞因子-细胞因子受体相互作用、ECM-受体相互作用等通路也显著富集。通过免疫浸润分析发现DN肾小管间质组织中记忆性静息CD4 T细胞、γδT细胞、静息肥大细胞、中性粒细胞上调,CD8 T细胞下调。最后机器学习筛选出MARCKSL1、CX3CR1、FSTL1、AGR2、GADD45B为核心基因,且具有良好的诊断与预测效能。结论:免疫失调和炎性反应、细胞因子作用、细胞外基质沉积为DN肾小管间质损伤核心病理机制,同时MARCKSL1、CX3CR1、FSTL1可能为DN诊断与预测潜在生物标志物。

Objective:To explore the genes related to renal tubulointerstitial injury in DN and to elucidate their underlying mechanism by using bioinformatics multi-chip joint analysis and machine learning technology,so as to provide new ideas for the di⁃agnosis and treatment of DN.Methods:Four gene expression datasets of DN tubulointerstitial tissues were retrieved from the GEO database.GSE30122,GSE47185 and GSE99340 were used as the combined microarray datasets,and GSE104954 was used as the independent verification datasets.The differentially expressed genes(DEGs)were identified by R language,and Gene Ontology(GO)enrichment,KEGG pathway enrichment,Gene Set Enrichment Analysis(GSEA)and Immune Cell Infiltration Analysis were performed.Furthermore,LASSO regression,SVM-RFE and RF machine learning algorithm were used to screen core genes,while external validation and Receiver Operating Curve(ROC)analysis as well as the model of prediction nomogram were performed.Finally,the influence of the clinical characteristics of DN patients was explored by Nephroseq.Results:A total of 107 DEGs were obtained,enrichment analysis revealed that the tubulointerstitial injury in DN was mainly involved in adaptive immune response,lymphocyte mediated immunity,regulation of immune effector process and immune-inflammatory pathways such as staphylococcus aureus infection,complement and coagulation cascades,phagosomes,and Th1 and Th2 cell differentia⁃tion.In addition,cell adhesion molecule,cytokine-cytokine receptor interaction and ECM-receptor interaction pathways were also significantly enriched.Memory resting CD4 T cells,γδT cells,resting mast cells and neutrophil cells were up-regulated,while CD8 T cells were down-regulated.Machine learning identified MARCKSL1、CX3CR1、FSTL1、AGR2、GADD45B as core genes with good diagnostic and predictive efficacy.Conclusion:The key pathological mechanism of tubulointerstitial injury in DN is im⁃mune disorder,inflammatory reaction,cytokine action and extracellular matrix deposition.Moreover,MARCKSL1、CX3CR1、FSTL1 may be the potential biomarkers for the diagnosis and prediction of DN.