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Identification of miRNAs-genes regulatory network in diabetic nephropathy based on bioinformatics analysis

MicroRNAs (miRNAs) play a great contribution to the development of diabetic nephropathy (DN). The aim of this study was to explore potential miRNAs-genes regulatory network and biomarkers for the pathogenesis of DN using bioinformatics methods. Gene expression profiling data related to DN (GSE1009)...

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Autores principales: Yang, Fengying, Cui, Zhenhai, Deng, Hongjun, Wang, Ying, Chen, Yang, Li, Huiqing, Yuan, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer Health 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6635158/
https://www.ncbi.nlm.nih.gov/pubmed/31277135
http://dx.doi.org/10.1097/MD.0000000000016225
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author Yang, Fengying
Cui, Zhenhai
Deng, Hongjun
Wang, Ying
Chen, Yang
Li, Huiqing
Yuan, Li
author_facet Yang, Fengying
Cui, Zhenhai
Deng, Hongjun
Wang, Ying
Chen, Yang
Li, Huiqing
Yuan, Li
author_sort Yang, Fengying
collection PubMed
description MicroRNAs (miRNAs) play a great contribution to the development of diabetic nephropathy (DN). The aim of this study was to explore potential miRNAs-genes regulatory network and biomarkers for the pathogenesis of DN using bioinformatics methods. Gene expression profiling data related to DN (GSE1009) was obtained from the Gene Expression Omnibus (GEO) database, and then differentially expressed genes (DEGs) between DN patients and normal individuals were screened using GEO2R, followed by a series of bioinformatics analyses, including identifying key genes, conducting pathway enrichment analysis, predicting and identifying key miRNAs, and establishing regulatory relationships between key miRNAs and their target genes. A total of 600 DEGs associated with DN were identified. An additional 7 key DEGs, including 6 downregulated genes, such as vascular endothelial growth factor α (VEGFA) and COL4A5, and 1 upregulated gene (CCL19), were identified in another dataset (GSE30528) from glomeruli samples. Pathway analysis showed that the down- and upregulated DEGs were enriched in 14 and 6 pathways, respectively, with 7 key genes mainly involved in extracellular matrix–receptor interaction, PI3K/Akt signaling, focal adhesion, and Rap1 signaling. The relationships between miRNAs and target genes were constructed, showing that miR-29 targeted COL4A and VEGFA, miR-200 targeted VEGFA, miR-25 targeted ITGAV, and miR-27 targeted EGFR. MiR-29 and miR-200 may play important roles in DN. VEGFA and COL4A5 were targeted by miR-29 and VEGFA by miR-200, which may mediate multiple signaling pathways leading to the pathogenesis and development of DN.
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spelling pubmed-66351582019-08-01 Identification of miRNAs-genes regulatory network in diabetic nephropathy based on bioinformatics analysis Yang, Fengying Cui, Zhenhai Deng, Hongjun Wang, Ying Chen, Yang Li, Huiqing Yuan, Li Medicine (Baltimore) Research Article MicroRNAs (miRNAs) play a great contribution to the development of diabetic nephropathy (DN). The aim of this study was to explore potential miRNAs-genes regulatory network and biomarkers for the pathogenesis of DN using bioinformatics methods. Gene expression profiling data related to DN (GSE1009) was obtained from the Gene Expression Omnibus (GEO) database, and then differentially expressed genes (DEGs) between DN patients and normal individuals were screened using GEO2R, followed by a series of bioinformatics analyses, including identifying key genes, conducting pathway enrichment analysis, predicting and identifying key miRNAs, and establishing regulatory relationships between key miRNAs and their target genes. A total of 600 DEGs associated with DN were identified. An additional 7 key DEGs, including 6 downregulated genes, such as vascular endothelial growth factor α (VEGFA) and COL4A5, and 1 upregulated gene (CCL19), were identified in another dataset (GSE30528) from glomeruli samples. Pathway analysis showed that the down- and upregulated DEGs were enriched in 14 and 6 pathways, respectively, with 7 key genes mainly involved in extracellular matrix–receptor interaction, PI3K/Akt signaling, focal adhesion, and Rap1 signaling. The relationships between miRNAs and target genes were constructed, showing that miR-29 targeted COL4A and VEGFA, miR-200 targeted VEGFA, miR-25 targeted ITGAV, and miR-27 targeted EGFR. MiR-29 and miR-200 may play important roles in DN. VEGFA and COL4A5 were targeted by miR-29 and VEGFA by miR-200, which may mediate multiple signaling pathways leading to the pathogenesis and development of DN. Wolters Kluwer Health 2019-07-05 /pmc/articles/PMC6635158/ /pubmed/31277135 http://dx.doi.org/10.1097/MD.0000000000016225 Text en Copyright © 2019 the Author(s). Published by Wolters Kluwer Health, Inc. http://creativecommons.org/licenses/by-nc-nd/4.0 This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0
spellingShingle Research Article
Yang, Fengying
Cui, Zhenhai
Deng, Hongjun
Wang, Ying
Chen, Yang
Li, Huiqing
Yuan, Li
Identification of miRNAs-genes regulatory network in diabetic nephropathy based on bioinformatics analysis
title Identification of miRNAs-genes regulatory network in diabetic nephropathy based on bioinformatics analysis
title_full Identification of miRNAs-genes regulatory network in diabetic nephropathy based on bioinformatics analysis
title_fullStr Identification of miRNAs-genes regulatory network in diabetic nephropathy based on bioinformatics analysis
title_full_unstemmed Identification of miRNAs-genes regulatory network in diabetic nephropathy based on bioinformatics analysis
title_short Identification of miRNAs-genes regulatory network in diabetic nephropathy based on bioinformatics analysis
title_sort identification of mirnas-genes regulatory network in diabetic nephropathy based on bioinformatics analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6635158/
https://www.ncbi.nlm.nih.gov/pubmed/31277135
http://dx.doi.org/10.1097/MD.0000000000016225
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