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 pr...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 diagnosis 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 differentiation.In addition,cell adhesion molecule,cytokine-cytokine receptor interaction and ECM-receptor interaction pathways were also significantly enriched.Memory resting CD4 T cells,γδ毮T 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 immune 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.展开更多
基金National Natural Science Foundation of China(No.81774273)。
文摘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 diagnosis 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 differentiation.In addition,cell adhesion molecule,cytokine-cytokine receptor interaction and ECM-receptor interaction pathways were also significantly enriched.Memory resting CD4 T cells,γδ毮T 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 immune 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.
