Identification of Key Candidate Genes and Chemical Perturbagens in Diabetic Kidney Disease Using Integrated Bioinformatics Analysis
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...
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2021-09-01
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doaj-8d031e2287c543468f8e4162df1d417b2021-09-07T07:58:40ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922021-09-011210.3389/fendo.2021.721202721202Identification of Key Candidate Genes and Chemical Perturbagens in Diabetic Kidney Disease Using Integrated Bioinformatics AnalysisZhuo Gao0Aishwarya S1Xiao-mei Li2Xin-lun Li3Li-na Sui4Department of Nephrology, Air Force Medical Center, Beijing, ChinaDepartment of Bioinformatics, Stella Maris College (Autonomous), Chennai, IndiaDepartment of Nephrology, Air Force Medical Center, Beijing, ChinaDepartment of Nephrology, Air Force Medical Center, Beijing, ChinaDepartment of Nephrology, Air Force Medical Center, Beijing, ChinaGlobally, 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.https://www.frontiersin.org/articles/10.3389/fendo.2021.721202/fulldiabetic nephropathygene ontologyprognosisbiomarkersdifferential gene expressionsdiabetic kidney disease |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhuo Gao Aishwarya S Xiao-mei Li Xin-lun Li Li-na Sui |
spellingShingle |
Zhuo Gao Aishwarya S Xiao-mei Li Xin-lun Li Li-na Sui Identification of Key Candidate Genes and Chemical Perturbagens in Diabetic Kidney Disease Using Integrated Bioinformatics Analysis Frontiers in Endocrinology diabetic nephropathy gene ontology prognosis biomarkers differential gene expressions diabetic kidney disease |
author_facet |
Zhuo Gao Aishwarya S Xiao-mei Li Xin-lun Li Li-na Sui |
author_sort |
Zhuo Gao |
title |
Identification of Key Candidate Genes and Chemical Perturbagens in Diabetic Kidney Disease Using Integrated Bioinformatics Analysis |
title_short |
Identification of Key Candidate Genes and Chemical Perturbagens in Diabetic Kidney Disease Using Integrated Bioinformatics Analysis |
title_full |
Identification of Key Candidate Genes and Chemical Perturbagens in Diabetic Kidney Disease Using Integrated Bioinformatics Analysis |
title_fullStr |
Identification of Key Candidate Genes and Chemical Perturbagens in Diabetic Kidney Disease Using Integrated Bioinformatics Analysis |
title_full_unstemmed |
Identification of Key Candidate Genes and Chemical Perturbagens in Diabetic Kidney Disease Using Integrated Bioinformatics Analysis |
title_sort |
identification of key candidate genes and chemical perturbagens in diabetic kidney disease using integrated bioinformatics analysis |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Endocrinology |
issn |
1664-2392 |
publishDate |
2021-09-01 |
description |
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. |
topic |
diabetic nephropathy gene ontology prognosis biomarkers differential gene expressions diabetic kidney disease |
url |
https://www.frontiersin.org/articles/10.3389/fendo.2021.721202/full |
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