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Identification of hub genes in diabetic kidney disease via multiple-microarray analysis

BACKGROUND: Diabetic kidney disease (DKD) is a leading cause of end-stage renal disease; however, the underlying molecular mechanisms remain unclear. Recently, bioinformatics analysis has provided a comprehensive insight toward the molecular mechanisms of DKD. Here, we re-analyzed three mRNA microar...

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Autores principales: Zhang, Yumin, Li, Wei, Zhou, Yunting
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7475500/
https://www.ncbi.nlm.nih.gov/pubmed/32953797
http://dx.doi.org/10.21037/atm-20-5171
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author Zhang, Yumin
Li, Wei
Zhou, Yunting
author_facet Zhang, Yumin
Li, Wei
Zhou, Yunting
author_sort Zhang, Yumin
collection PubMed
description BACKGROUND: Diabetic kidney disease (DKD) is a leading cause of end-stage renal disease; however, the underlying molecular mechanisms remain unclear. Recently, bioinformatics analysis has provided a comprehensive insight toward the molecular mechanisms of DKD. Here, we re-analyzed three mRNA microarray datasets including a single-cell RNA sequencing (scRNA-seq) dataset, with the aim of identifying crucial genes correlated with DKD and contribute to a better understanding of DKD pathogenesis. METHODS: Three datasets including GSE131882, GSE30122, and GSE30529 were utilized to find differentially expressed genes (DEGs). The potential functions of DEGs were analyzed by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. A protein-protein interaction (PPI) network was constructed, and hub genes were selected with the top three molecular complex detection (MCODE) score. A correlation analysis between hub genes and clinical indicators was also performed. RESULTS: In total, 84 upregulated DEGs and 49 downregulated DEGs were identified. Enriched pathways of the upregulated DEGs included extracellular matrix (ECM) receptor interaction, focal adhesion, human papillomavirus infection, malaria, and cell adhesion molecules. The downregulated DEGs were mainly enriched in ascorbate and aldarate metabolism, arginine and proline metabolism, endocrine- and other factor-regulated calcium reabsorption, mineral absorption and longevity regulating pathway, and multiple species signaling pathway. Seventeen hub genes were identified, and correlation analysis between unexplored hub genes and clinical features of DKD suggested that EGF, KNG1, GADD45B, and CDH2 might have reno-protective roles in DKD. Meanwhile, ATF3, B2M, VCAM1, CLDN4, SPP1, SOX9, JAG1, C3, and CD24 might promote the progression of DKD. Finally, most hub genes were found present in the immune cells of diabetic kidneys, which suggest the important role of inflammation infiltration in DKD pathogenesis. CONCLUSIONS: In this study, we found seventeen hub genes using a scRNA-seq contained multiple-microarray analysis, which enriched the present understanding of molecular mechanisms underlying the pathogenesis of DKD in cells’ level and provided candidate targets for diagnosis and treatment of DKD.
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spelling pubmed-74755002020-09-17 Identification of hub genes in diabetic kidney disease via multiple-microarray analysis Zhang, Yumin Li, Wei Zhou, Yunting Ann Transl Med Original Article BACKGROUND: Diabetic kidney disease (DKD) is a leading cause of end-stage renal disease; however, the underlying molecular mechanisms remain unclear. Recently, bioinformatics analysis has provided a comprehensive insight toward the molecular mechanisms of DKD. Here, we re-analyzed three mRNA microarray datasets including a single-cell RNA sequencing (scRNA-seq) dataset, with the aim of identifying crucial genes correlated with DKD and contribute to a better understanding of DKD pathogenesis. METHODS: Three datasets including GSE131882, GSE30122, and GSE30529 were utilized to find differentially expressed genes (DEGs). The potential functions of DEGs were analyzed by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. A protein-protein interaction (PPI) network was constructed, and hub genes were selected with the top three molecular complex detection (MCODE) score. A correlation analysis between hub genes and clinical indicators was also performed. RESULTS: In total, 84 upregulated DEGs and 49 downregulated DEGs were identified. Enriched pathways of the upregulated DEGs included extracellular matrix (ECM) receptor interaction, focal adhesion, human papillomavirus infection, malaria, and cell adhesion molecules. The downregulated DEGs were mainly enriched in ascorbate and aldarate metabolism, arginine and proline metabolism, endocrine- and other factor-regulated calcium reabsorption, mineral absorption and longevity regulating pathway, and multiple species signaling pathway. Seventeen hub genes were identified, and correlation analysis between unexplored hub genes and clinical features of DKD suggested that EGF, KNG1, GADD45B, and CDH2 might have reno-protective roles in DKD. Meanwhile, ATF3, B2M, VCAM1, CLDN4, SPP1, SOX9, JAG1, C3, and CD24 might promote the progression of DKD. Finally, most hub genes were found present in the immune cells of diabetic kidneys, which suggest the important role of inflammation infiltration in DKD pathogenesis. CONCLUSIONS: In this study, we found seventeen hub genes using a scRNA-seq contained multiple-microarray analysis, which enriched the present understanding of molecular mechanisms underlying the pathogenesis of DKD in cells’ level and provided candidate targets for diagnosis and treatment of DKD. AME Publishing Company 2020-08 /pmc/articles/PMC7475500/ /pubmed/32953797 http://dx.doi.org/10.21037/atm-20-5171 Text en 2020 Annals of Translational Medicine. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Zhang, Yumin
Li, Wei
Zhou, Yunting
Identification of hub genes in diabetic kidney disease via multiple-microarray analysis
title Identification of hub genes in diabetic kidney disease via multiple-microarray analysis
title_full Identification of hub genes in diabetic kidney disease via multiple-microarray analysis
title_fullStr Identification of hub genes in diabetic kidney disease via multiple-microarray analysis
title_full_unstemmed Identification of hub genes in diabetic kidney disease via multiple-microarray analysis
title_short Identification of hub genes in diabetic kidney disease via multiple-microarray analysis
title_sort identification of hub genes in diabetic kidney disease via multiple-microarray analysis
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7475500/
https://www.ncbi.nlm.nih.gov/pubmed/32953797
http://dx.doi.org/10.21037/atm-20-5171
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