Integrative analyses of biomarkers and pathways for diabetic nephropathy.

Background: Diabetic nephropathy (DN) is a widespread diabetic complication and a major cause of terminal kidney disease. There is no doubt that DN is a chronic disease that imposes substantial health and economic burdens on the world's populations. By now, several important and exciting advances have been made in research on etiopathogenesis. Therefore, the genetic mechanisms underlying these effects remain unknown. Methods: The GSE30122, GSE30528, and GSE30529 microarray datasets were downloaded from the Gene Expression Omnibus database (GEO). Analyses of differentially expressed genes (DEGs), enrichment of gene ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed. Protein-protein interaction (PPI) network construction was completed by the STRING database. Hub genes were identified by Cytoscape software, and common hub genes were identified by taking intersection sets. The diagnostic value of common hub genes was then predicted in the GSE30529 and GSE30528 datasets. Further analysis was carried out on the modules to identify transcription factors and miRNA networks. As well, a comparative toxicogenomics database was used to assess interactions between potential key genes and diseases associated upstream of DN. Results: Samples from 19 DNs and 50 normal controls were identified in the GSE30122 dataset. 86 upregulated genes and 34 downregulated genes (a total of 120 DEGs). GO analysis showed significant enrichment in humoral immune response, protein activation cascade, complement activation, extracellular matrix, glycosaminoglycan binding, and antigen binding. KEGG analysis showed significant enrichment in complement and coagulation cascades, phagosomes, the Rap1 signaling pathway, the PI3K-Akt signaling pathway, and infection. GSEA was mainly enriched in the TYROBP causal network, the inflammatory response pathway, chemokine receptor binding, the interferon signaling pathway, ECM receptor interaction, and the integrin 1 pathway. Meanwhile, mRNA-miRNA and mRNA-TF networks were constructed for common hub genes. Nine pivotal genes were identified by taking the intersection. After validating the expression differences and diagnostic values of the GSE30528 and GSE30529 datasets, eight pivotal genes (TYROBP, ITGB2, CD53, IL10RA, LAPTM5, CD48, C1QA, and IRF8) were finally identified as having diagnostic values. Conclusion: Pathway enrichment analysis scores provide insight into the genetic phenotype and may propose molecular mechanisms of DN. The target genes TYROBP, ITGB2, CD53, IL10RA, LAPTM5, CD48, C1QA, and IRF8 are promising new targets for DN. SPI1, HIF1A, STAT1, KLF5, RUNX1, MBD1, SP1, and WT1 may be involved in the regulatory mechanisms of DN development. Our study may provide a potential biomarker or therapeutic locus for the study of DN.

Li B ，Zhao X ，Xie W ，Hong Z ，Zhang Y ... -  《Frontiers in Genetics》

Identifying C1QB, ITGAM, and ITGB2 as potential diagnostic candidate genes for diabetic nephropathy using bioinformatics analysis.

Diabetic nephropathy (DN), the most intractable complication in diabetes patients, can lead to proteinuria and progressive reduction of glomerular filtration rate (GFR), which seriously affects the quality of life of patients and is associated with high mortality. However, the lack of accurate key candidate genes makes diagnosis of DN very difficult. This study aimed to identify new potential candidate genes for DN using bioinformatics, and elucidated the mechanism of DN at the cellular transcriptional level. The microarray dataset GSE30529 was downloaded from the Gene Expression Omnibus Database (GEO), and the differentially expressed genes (DEGs) were screened by R software. We used Gene Ontology (GO), gene set enrichment analysis (GSEA), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to identify the signal pathways and genes. Protein-protein interaction (PPI) networks were constructed using the STRING database. The GSE30122 dataset was selected as the validation set. Receiver operating characteristic (ROC) curves were applied to evaluate the predictive value of genes. An area under curve (AUC) greater than 0.85 was considered to be of high diagnostic value. Several online databases were used to predict miRNAs and transcription factors (TFs) capable of binding hub genes. Cytoscape was used for constructing a miRNA-mRNA-TF network. The online database 'nephroseq' predicted the correlation between genes and kidney function. The serum level of creatinine, BUN, and albumin, and the urinary protein/creatinine ratio of the DN rat model were detected. The expression of hub genes was further verified through qPCR. Data were analyzed statistically using Student's t-test by the 'ggpubr' package. A total of 463 DEGs were identified from GSE30529. According to enrichment analysis, DEGs were mainly enriched in the immune response, coagulation cascades, and cytokine signaling pathways. Twenty hub genes with the highest connectivity and several gene cluster modules were ensured using Cytoscape. Five high diagnostic hub genes were selected and verified by GSE30122. The MiRNA-mRNA-TF network suggested a potential RNA regulatory relationship. Hub gene expression was positively correlated with kidney injury. The level of serum creatinine and BUN in the DN group was higher than in the control group (unpaired t test, t = 3.391, df = 4, p = 0.0275, r = 0.861). Meanwhile, the DN group had a higher urinary protein/creatinine ratio (unpaired t test, t = 17.23, df = 16, p < 0.001, r = 0.974). QPCR results showed that the potential candidate genes for DN diagnosis included C1QB, ITGAM, and ITGB2. We identified C1QB, ITGAM and ITGB2 as potential candidate genes for DN diagnosis and therapy and provided insight into the mechanisms of DN development at transcriptome level. We further completed the construction of miRNA-mRNA-TF network to propose potential RNA regulatory pathways adjusting disease progression in DN.

Hu Y ，Yu Y ，Dong H ，Jiang W ... -  《PeerJ》

Bioinformatics Analysis of the Mechanisms of Diabetic Nephropathy via Novel Biomarkers and Competing Endogenous RNA Network.

Diabetic nephropathy (DN) is one of the common chronic complications of diabetes with unclear molecular mechanisms, which is associated with end-stage renal disease (ESRD) and chronic kidney disease (CKD). Our study intended to construct a competing endogenous RNA (ceRNA) network via bioinformatics analysis to determine the potential molecular mechanisms of DN pathogenesis. The microarray datasets (GSE30122 and GSE30529) were downloaded from the Gene Expression Omnibus database to find differentially expressed genes (DEGs). GSE51674 and GSE155188 datasets were used to identified the differentially expressed microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), respectively. The DEGs between normal and DN renal tissues were performed using the Linear Models for Microarray (limma) package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to reveal the mechanisms of DEGs in the progression of DN. The protein-protein interactions (PPI) of DEGs were carried out by STRING database. The lncRNA-miRNA-messenger RNA (mRNA) ceRNA network was constructed and visualized via Cytoscape on the basis of the interaction generated through the miRDB and TargetScan databases. A total of 94 significantly upregulated and 14 downregulated mRNAs, 31 upregulated and 121 downregulated miRNAs, and nine upregulated and 81 downregulated lncRNAs were identified. GO and KEGG pathways enriched in several functions and expression pathways, such as inflammatory response, immune response, identical protein binding, nuclear factor kappa b (NF-κB) signaling pathway, and PI3K-Akt signaling pathway. Based on the analysis of the ceRNA network, five differentially expressed lncRNAs (DElncRNAs) (SNHG6, KCNMB2-AS1, LINC00520, DANCR, and PCAT6), five DEmiRNAs (miR-130b-5p, miR-326, miR-374a-3p, miR-577, and miR-944), and five DEmRNAs (PTPRC, CD53, IRF8, IL10RA, and LAPTM5) were demonstrated to be related to the pathogenesis of DN. The hub genes were validated by using receiver operating characteristic curve (ROC) and real-time PCR (RT-PCR). Our research identified hub genes related to the potential mechanism of DN and provided new lncRNA-miRNA-mRNA ceRNA network that contributed to diagnostic and potential therapeutic targets for DN.

Guo M ，Dai Y ，Jiang L ，Gao J ... -  《Frontiers in Endocrinology》

Investigation of the Mechanism of Complement System in Diabetic Nephropathy via Bioinformatics Analysis.

Diabetic nephropathy (DN) is a major cause of end-stage renal disease (ESRD) throughout the world, and the identification of novel biomarkers via bioinformatics analysis could provide research foundation for future experimental verification and large-group cohort in DN models and patients. GSE30528, GSE47183, and GSE104948 were downloaded from Gene Expression Omnibus (GEO) database to find differentially expressed genes (DEGs). The difference of gene expression between normal renal tissues and DN renal tissues was firstly screened by GEO2R. Then, the protein-protein interactions (PPIs) of DEGs were performed by STRING database, the result was integrated and visualized via applying Cytoscape software, and the hub genes in this PPI network were selected by MCODE and topological analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to determine the molecular mechanisms of DEGs involved in the progression of DN. Finally, the Nephroseq v5 online platform was used to explore the correlation between hub genes and clinical features of DN. There were 64 DEGs, and 32 hub genes were identified, enriched pathways of hub genes involved in several functions and expression pathways, such as complement binding, extracellular matrix structural constituent, complement cascade related pathways, and ECM proteoglycans. The correlation analysis and subgroup analysis of 7 complement cascade-related hub genes and the clinical characteristics of DN showed that C1QA, C1QB, C3, CFB, ITGB2, VSIG4, and CLU may participate in the development of DN. We confirmed that the complement cascade-related hub genes may be the novel biomarkers for DN early diagnosis and targeted treatment.

Xu B ，Wang L ，Zhan H ，Zhao L ，Wang Y ，Shen M ，Xu K ，Li L ，Luo X ，Zhou S ，Tang A ，Liu G ，Song L ，Li Y ... -  《-》

Identification of co-expressed central genes and transcription factors in acute myocardial infarction and diabetic nephropathy.

Acute myocardial infarction (AMI) and diabetic nephropathy (DN) are common clinical co-morbidities, but they are challenging to manage and have poor prognoses. There is no research on the bioinformatics mechanisms of comorbidity, and this study aims to investigate such mechanisms. We downloaded the AMI data (GSE66360) and DN datasets (GSE30528 and GSE30529) from the Gene Expression Omnibus (GEO) platform. The GSE66360 dataset was divided into two parts: the training set and the validation set, and GSE30529 was used as the training set and GSE30528 as the validation set. After identifying the common differentially expressed genes (DEGs) in AMI and DN in the training set, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and protein-protein interaction (PPI) network construction were performed. A sub-network graph was constructed by MCODE, and 15 hub genes were screened by the Cytohubba plugin. The screened hub genes were validated, and the 15 screened hub genes were subjected to GO, KEGG, Gene MANIA analysis, and transcription factor (TF) prediction. Finally, we performed TF differential analysis, enrichment analysis, and TF and gene regulatory network construction. A total of 46 genes (43 up-regulated and 3 down-regulated) were identified for subsequent analysis. GO functional analysis emphasized the presence of genes mainly in the vesicle membrane and secretory granule membrane involved in antigen processing and presentation, lipopeptide binding, NAD + nucleosidase activity, and Toll-like receptor binding. The KEGG pathways analyzed were mainly in the phagosome, neutrophil extracellular trap formation, natural killer cell-mediated cytotoxicity, apoptosis, Fc gamma R-mediated phagocytosis, and Toll-like receptor signaling pathways. Eight co-expressed hub genes were identified and validated, namely TLR2, FCER1G, CD163, CTSS, CLEC4A, IGSF6, NCF2, and MS4A6A. Three transcription factors were identified and validated in AMI, namely NFKB1, HIF1A, and SPI1. Our study reveals the common pathogenesis of AMI and DN. These common pathways and hub genes may provide new ideas for further mechanistic studies.

Li B ，Zhao X ，Xie W ，Hong Z ，Cao Y ，Zhang Y ，Ding Y ... -  《BMC Medical Genomics》