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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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Autores principales: Gao, Zhuo, S, Aishwarya, Li, Xiao-mei, Li, Xin-lun, Sui, Li-na
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8453249/
https://www.ncbi.nlm.nih.gov/pubmed/34557161
http://dx.doi.org/10.3389/fendo.2021.721202
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author Gao, Zhuo
S, Aishwarya
Li, Xiao-mei
Li, Xin-lun
Sui, Li-na
author_facet Gao, Zhuo
S, Aishwarya
Li, Xiao-mei
Li, Xin-lun
Sui, Li-na
author_sort Gao, Zhuo
collection PubMed
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.
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spelling pubmed-84532492021-09-22 Identification of Key Candidate Genes and Chemical Perturbagens in Diabetic Kidney Disease Using Integrated Bioinformatics Analysis Gao, Zhuo S, Aishwarya Li, Xiao-mei Li, Xin-lun Sui, Li-na Front Endocrinol (Lausanne) Endocrinology 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. Frontiers Media S.A. 2021-09-07 /pmc/articles/PMC8453249/ /pubmed/34557161 http://dx.doi.org/10.3389/fendo.2021.721202 Text en Copyright © 2021 Gao, S, Li, Li and Sui https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Endocrinology
Gao, Zhuo
S, Aishwarya
Li, Xiao-mei
Li, Xin-lun
Sui, Li-na
Identification of Key Candidate Genes and Chemical Perturbagens in Diabetic Kidney Disease Using Integrated Bioinformatics Analysis
title 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_short 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
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8453249/
https://www.ncbi.nlm.nih.gov/pubmed/34557161
http://dx.doi.org/10.3389/fendo.2021.721202
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