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Identification of Key Candidate Genes and Chemical Perturbagens in Diabetic Kidney Disease Using Integrated Bioinformatics Analysis.

ABSTRACT: Globally, nearly 40 percent of all diabetic patients develop serious diabetic kidney disease (DKD). The identification of the potential early-stage biomarkers and elucidation of their underlying molecular mechanisms in DKD are required. In this study, we performed integrated bioinformatics analysis on the expression profiles GSE111154, GSE30528 and GSE30529 associated with early diabetic nephropathy (EDN), glomerular DKD (GDKD) and tubular DKD (TDKD), respectively. A total of 1,241, 318 and 280 differentially expressed genes (DEGs) were identified for GSE30258, GSE30529, and GSE111154 respectively. Subsequently, 280 upregulated and 27 downregulated DEGs shared between the three GSE datasets were identified. Further analysis of the gene expression levels conducted on the hub genes revealed SPARC (Secreted Protein Acidic And Cysteine Rich), POSTN (periostin), LUM (Lumican), KNG1 (Kininogen 1), FN1 (Fibronectin 1), VCAN (Versican) and PTPRO (Protein Tyrosine Phosphatase Receptor Type O) having potential roles in DKD progression. FN1, LUM and VCAN were identified as upregulated genes for GDKD whereas the downregulation of PTPRO was associated with all three diseases. Both POSTN and SPARC were identified as the overexpressed putative biomarkers whereas KNG1 was found as downregulated in TDKD. Additionally, we also identified two drugs, namely pidorubicine, a topoisomerase inhibitor (LINCS ID- BRD-K04548931) and Polo-like kinase inhibitor (LINCS ID- BRD-K41652870) having the validated role in reversing the differential gene expression patterns observed in the three GSE datasets used. Collectively, this study aids in the understanding of the molecular drivers, critical genes and pathways that underlie DKD initiation and progression.

SUBMITTER: Gao Z

PROVIDER: S-EPMC8453249 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Objective: The currently established diagnostic and prognostic tools for diabetic kidney disease (DKD) have limitations, which demands the necessity to find new genes and pathways associated with diagnosis and treatment. Our study aims to reveal the gene expression alteration and discover critical genes involved in the development of DKD, thus providing novel diagnostic molecular markers and therapeutic targets. Materials and methods: The differences of infiltrating immune cells within kidney were compared between healthy living donors and DKD patients. Besides, differentially expressed genes (DEGs) within kidney from healthy living donor, early stage DKD and advanced stage DKD samples were detected. Furthermore, the weighted co-expressed network (WGCNA) and protein-protein interaction (PPI) network were constructed, followed by recognition of core hub genes and module analysis. Receiver operating characteristic (ROC) curve analysis was implemented to determine the diagnostic value of hub genes, correlation analysis was employed to explore the association between hub genes and infiltrating immune cells, and certain hub genes was validated by quantitative real-time PCR and immunohistochemistry staining in cultured tubule cells and diabetic mice kidney. Finally, the candidate small molecules as potential drugs to treat DKD were anticipated through utilizing virtual screening and molecular docking investigation. Results: Our study revealed significantly higher proportion of infiltrating immune cells within kidney from DKD patients via probing the immune landscape by single-cell transcriptomics. Besides, 126 commonly shared DEGs identified among three group samples were enriched in immune biological process. In addition, the ROC curve analysis demonstrated the strong diagnostic accuracy of recognized hub genes (NFKB1, DYRK2, ATAD2, YAP1, and CHD3) from PPI network. Correlation analysis further confirmed the positive association between these hub genes with infiltrating natural killer cells. More importantly, the mRNA transcripts and protein abundance of YAP1 were significantly higher in high glucose-treated renal tubule cells and diabetic mice kidney, and the small molecules exhibiting the best binding affinities with YAP1 were predicted and acquired. Conclusion: Our findings for the first time indicate that NFKB1, DYRK2, ATAD2, YAP1, and CHD3 might be potential novel biomarkers and therapeutic targets for DKD, providing insights into the molecular mechanisms underlying the pathogenesis of DKD.

Project description:Background:Diabetes mellitus is becoming a significant health problem with the International Diabetes Federation (IDF) expecting a startling 642 million diabetes patients by 2040. Liraglutide, a glucagon-like peptide-1 (GLP-1) analog, is reported to protect against diabetic cardiomyopathy by binding to the receptor, GLP-1R. However, the underlying mechanism has yet to be clarified. This study aimed to investigate the underlying mechanisms and the effects of liraglutide on diabetic patient's cardiac muscles. Methods:GSE102194 genetic expression profiles were extracted from the Gene Expression Omnibus (GEO) database. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were carried out. Next, Cytoscape software was used to construct the protein-protein interaction (PPI) network of the differentially expressed genes (DEGs). DEGs were mapped onto a protein-protein interaction (PPI) network that comprised 249 nodes and 776 edges. Results:A total of 520 DEGs were discovered, including 159 down-regulated genes and 361 up-regulated genes. DEGs that were upregulated were notably enriched in biological processes (BP) such as muscle system process, muscle system process, muscle structure development and anatomical structure morphogenesis while DEGs that were downregulated were rich in detection of chemical stimulus and neurological system process. KEGG pathway analysis showed the up-regulated DEGs were enriched in adrenergic signaling for cardiomyocytes, dopaminergic synapse, and circadian entrainment, while the down-regulated DEGs were enriched for factory transduction in 249 of the 520 tested samples. The modular analysis identified 4 modules that participated in some pathways associated with cardiac muscle contraction, hypertrophic cardiomyopathy (HCM), and MAPK signaling pathway. Conclusions:Our data showed that Glp-1 could decrease the protein expression of p38, JNK, ERK1/2, and MARS proteins induced by high glucose (22 mM, 72 h). This study highlights the potential physiological processes that take place in diabetic cardiac muscles exposed to liraglutide. Our findings elucidated the regulatory network in diabetic cardiomyopathy and might provide a novel diagnostic and therapeutic target for diabetic cardiomyopathy.

Project description:BackgroundAtherosclerosis (AS) is a chronic inflammatory disease that might induce severe cardiovascular events, such as myocardial infarction and cerebral infarction. These risk factors in the pathogenesis of AS remain uncertain and further research is needed. This study aims to explore the potential molecular mechanisms of AS by bioinformatics analyses.MethodsGSE100927 gene expression profiles, including 69 AS samples and 35 healthy controls, were downloaded from Gene Expression Omnibus database and indenfied for key genes and pathways in AS.ResultsA total of 443 differentially expressed genes (DEGs) between control and AS were identified, including 323 down-regulated genes and 120 up-regulated genes. The Gene ontology terms enriched by the up-regulated DEGs were associated with the regulation of leukocyte activation, endocytic vesicle, and cytokine binding, while the down-regulated DEGs were associated with negative regulation of cell growth, extracellular matrix, and G protein-coupled receptor binding. KEGG pathway analysis showed that the up-regulated DEGs were enriched in Osteoclast differentiation and Phagosome, while the down-regulated DEGs were enriched in vascular smooth muscle contraction and cGMP-PKG signaling pathway. Using the modular analysis of Cytoscape, we identified 3 modules mainly involved in Leishmaniasis and Osteoclast differentiation. The GSEA analysis showed the up-regulated gene sets were enriched in the ribosome, ascorbated metabolism, and propanoate metabolism. The LASSO Cox regression analysis showed the top 3 genes were TNF, CX3CR1, and COL1R1. Finally, we found these immune cells were conferred significantly higher infiltrating density in the AS group.ConclusionsOur data showed the pathway of Osteoclast differentiation and Leishmaniasis was involved in the AS process and we developed a three-gene model base on the prognosis of AS. These findings clarified the gene regulatory network of AS and may provide a novel target for AS therapy.

Project description:Wilms tumor (WT) is one of the most common types of pediatric solid tumors; however, its molecular mechanisms remain unclear. The present study aimed to identify key genes and microRNAs (miRNAs), and to predict the underlying molecular mechanisms of WT using integrated bioinformatics analysis. Original gene expression profiles were downloaded from the Gene Expression Omnibus (GEO; accession, GSE66405) and The Cancer Genome Atlas (TCGA) databases. Similarly, miRNA expression patterns were downloaded from GEO (accession, GSE57370) and TCGA. R version 3.5.0 software was used to identify differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) using the limma and edgeR packages. Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology analyses were performed to examine the biological functions of the DEGs. Additionally, a protein-protein interaction (PPI) network was constructed to screen hub gene modules using Cytoscape software. By predicting target genes of the DEMs and integrating them with DEGs, the present study constructed a miRNA-mRNA regulatory network to predict the possible molecular mechanism of WT. Expression of hub genes was validated using the Oncomine database. A total of 613 genes and 29 miRNAs were identified to be differentially expressed in WT. By constructing a PPI network and screening hub gene modules, 5 upregulated genes, including BUB1 mitotic checkpoint serine/threonine kinase, BUB1B mitotic checkpoint serine/threonine kinase B, cell division cycle protein 45, cyclin B2 and pituitary tumor-transforming 1. These genes were identified to be associated with the cell cycle pathway, which suggested that these genes may serve important roles in WT. In addition, a miRNA-mRNA regulatory network was constructed and comprised 16 DEMs and 19 DEGs. In conclusion, key genes, miRNAs and the mRNA-miRNA regulatory network identified in the present study may improve understanding of the underlying molecular mechanisms in the occurrence and development of WT, and may aid the identification of potential biomarkers and therapeutic targets.

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Identification of Key Candidate Genes and Chemical Perturbagens in Diabetic Kidney Disease Using Integrated Bioinformatics Analysis.

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