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Exploring the pathogenesis of diabetic kidney disease by microarray data analysis
Diabetic kidney disease (DKD) is a major complication of diabetes mellitus, and the leading contributor of end-stage renal disease. Hence, insights into the molecular pathogenesis of DKD are urgently needed. The purpose of this article is to reveal the molecular mechanisms underlying the pathogenesi...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9428563/ https://www.ncbi.nlm.nih.gov/pubmed/36059966 http://dx.doi.org/10.3389/fphar.2022.932205 |
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author | Cao, Haiyan Rao, Xiaosheng Jia, Junya Yan, Tiekun Li, Dong |
author_facet | Cao, Haiyan Rao, Xiaosheng Jia, Junya Yan, Tiekun Li, Dong |
author_sort | Cao, Haiyan |
collection | PubMed |
description | Diabetic kidney disease (DKD) is a major complication of diabetes mellitus, and the leading contributor of end-stage renal disease. Hence, insights into the molecular pathogenesis of DKD are urgently needed. The purpose of this article is to reveal the molecular mechanisms underlying the pathogenesis of DKD. The microarray datasets of GSE30528 and GSE30529 were downloaded from the NCBI Gene Expression Omnibus (GEO) database to identify the common differentially expressed genes (DEGs) between the glomerular DKD (GDKD) and tubular DKD (TDKD), respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to analyze the function and pathways of the common DEGs. After constructing the protein–protein interaction (PPI) network and subnetwork analysis, three types of analyses were performed, namely, identification of hub genes, analysis of the coexpressed network, and exploration of transcription factors (TFs). Totally, 348 and 463 DEGs were identified in GDKD and TDKD, respectively. Then, 66 common DEGs (63 upregulated DEGs and three downregulated DEGs) were obtained in DKD patients. GO and KEGG pathway analyses revealed the importance of inflammation response, immune-related pathways, and extracellular matrix-related pathways, especially chemokines and cytokines, in DKD. Fifteen hub genes from the 66 common DEGs, namely, IL10RA, IRF8, LY86, C1QA, C1QB, CD53, CD1C, CTSS, CCR2, CD163, CCL5, CD48, RNASE6, CD52, and CD2 were identified. In summary, through the microarray data analysis, the common functions and hub genes greatly contribute to the elucidation of the molecular pathogenesis associated with DKD. |
format | Online Article Text |
id | pubmed-9428563 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94285632022-09-01 Exploring the pathogenesis of diabetic kidney disease by microarray data analysis Cao, Haiyan Rao, Xiaosheng Jia, Junya Yan, Tiekun Li, Dong Front Pharmacol Pharmacology Diabetic kidney disease (DKD) is a major complication of diabetes mellitus, and the leading contributor of end-stage renal disease. Hence, insights into the molecular pathogenesis of DKD are urgently needed. The purpose of this article is to reveal the molecular mechanisms underlying the pathogenesis of DKD. The microarray datasets of GSE30528 and GSE30529 were downloaded from the NCBI Gene Expression Omnibus (GEO) database to identify the common differentially expressed genes (DEGs) between the glomerular DKD (GDKD) and tubular DKD (TDKD), respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to analyze the function and pathways of the common DEGs. After constructing the protein–protein interaction (PPI) network and subnetwork analysis, three types of analyses were performed, namely, identification of hub genes, analysis of the coexpressed network, and exploration of transcription factors (TFs). Totally, 348 and 463 DEGs were identified in GDKD and TDKD, respectively. Then, 66 common DEGs (63 upregulated DEGs and three downregulated DEGs) were obtained in DKD patients. GO and KEGG pathway analyses revealed the importance of inflammation response, immune-related pathways, and extracellular matrix-related pathways, especially chemokines and cytokines, in DKD. Fifteen hub genes from the 66 common DEGs, namely, IL10RA, IRF8, LY86, C1QA, C1QB, CD53, CD1C, CTSS, CCR2, CD163, CCL5, CD48, RNASE6, CD52, and CD2 were identified. In summary, through the microarray data analysis, the common functions and hub genes greatly contribute to the elucidation of the molecular pathogenesis associated with DKD. Frontiers Media S.A. 2022-08-17 /pmc/articles/PMC9428563/ /pubmed/36059966 http://dx.doi.org/10.3389/fphar.2022.932205 Text en Copyright © 2022 Cao, Rao, Jia, Yan and Li. 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 | Pharmacology Cao, Haiyan Rao, Xiaosheng Jia, Junya Yan, Tiekun Li, Dong Exploring the pathogenesis of diabetic kidney disease by microarray data analysis |
title | Exploring the pathogenesis of diabetic kidney disease by microarray data analysis |
title_full | Exploring the pathogenesis of diabetic kidney disease by microarray data analysis |
title_fullStr | Exploring the pathogenesis of diabetic kidney disease by microarray data analysis |
title_full_unstemmed | Exploring the pathogenesis of diabetic kidney disease by microarray data analysis |
title_short | Exploring the pathogenesis of diabetic kidney disease by microarray data analysis |
title_sort | exploring the pathogenesis of diabetic kidney disease by microarray data analysis |
topic | Pharmacology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9428563/ https://www.ncbi.nlm.nih.gov/pubmed/36059966 http://dx.doi.org/10.3389/fphar.2022.932205 |
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