Objective To identify the key differentially expressed genes in the patients with lupus nephritis(LN)using bioinformatics methods and discover potential diagnostic biomarkers of LN for exploring the pathogenic mechanisms of LN.Methods The GSE99339 dataset related to chronic kidney disease was obtained from the Gene Expression Omnibus(GEO)database.Weighted gene co-expres-sion network analysis(WGCNA)was performed to identify the core modules highly correlated with LN.The differentially expressed genes(DEGs)between the two groups(32 LN patients and 14 healthy controls)from GSE32591 dataset were identified using Limma package.The intersection of DEGs and the LN-related module genes was obtained.The Gene Ontology(GO)function annotation and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analyses of intersection genes were conducted using DAVID online tool to construct protein-protein interaction(PPI)network,and the key genes were screened using Cytoscape software.Boxplots and receiver operating characteristic(ROC)curves were generated to evaluate the diagnostic efficiency of the key genes based on the the gene expression data of GSE112943 validation dataset.Results The greenyellow module(102 genes)was strongly correlated with LN(r=0.85,P＜0.01).A total of 361 DEGs were identified from GSE32591 dataset,among which 53 genes were found to intersect with the green-yellow module genes.The LN-related genes were mainly enriched in the biological processes and pathways which related to antiviral response,innate immunity,hepatitis C,and influenza A virus.The top five key genes in the PPI network were IFIT3,MX1,OAS1,RSAD2,and OAS3.The expression levels of 1FIT3,MX1,OAS1 and RSAD2 in LN were significantly different from con-trol groups(P＜0.05).ROC curve analysis showed that the AUC values for the four genes in predicting LN were all greater than 0.8,indicating high diagnostic efficiency.Conclusion IFIT3,MX1,OAS1 and RSAD2 may be the key differentially expressed genes in LN and may be potential diagnostic biomarkers and therapeutic targets for LN.

Objective To identify the key differentially expressed genes in the patients with lupus nephritis(LN)using bioinformatics methods and discover potential diagnostic biomarkers of LN for exploring the pathogenic mechanisms of LN.Methods The GSE99339 dataset related to chronic kidney disease was obtained from the Gene Expression Omnibus(GEO)database.Weighted gene co-expres-sion network analysis(WGCNA)was performed to identify the core modules highly correlated with LN.The differentially expressed genes(DEGs)between the two groups(32 LN patients and 14 healthy controls)from GSE32591 dataset were identified using Limma package.The intersection of DEGs and the LN-related module genes was obtained.The Gene Ontology(GO)function annotation and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analyses of intersection genes were conducted using DAVID online tool to construct protein-protein interaction(PPI)network,and the key genes were screened using Cytoscape software.Boxplots and receiver operating characteristic(ROC)curves were generated to evaluate the diagnostic efficiency of the key genes based on the the gene expression data of GSE112943 validation dataset.Results The greenyellow module(102 genes)was strongly correlated with LN(r=0.85,P＜0.01).A total of 361 DEGs were identified from GSE32591 dataset,among which 53 genes were found to intersect with the green-yellow module genes.The LN-related genes were mainly enriched in the biological processes and pathways which related to antiviral response,innate immunity,hepatitis C,and influenza A virus.The top five key genes in the PPI network were IFIT3,MX1,OAS1,RSAD2,and OAS3.The expression levels of 1FIT3,MX1,OAS1 and RSAD2 in LN were significantly different from con-trol groups(P＜0.05).ROC curve analysis showed that the AUC values for the four genes in predicting LN were all greater than 0.8,indicating high diagnostic efficiency.Conclusion IFIT3,MX1,OAS1 and RSAD2 may be the key differentially expressed genes in LN and may be potential diagnostic biomarkers and therapeutic targets for LN.