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The unique molecular mechanism of diabetic nephropathy: a bioinformatics analysis of over 250 microarray datasets

BACKGROUND/AIMS: Diabetic nephropathy (DN) is one of the main causes of end-stage kidney disease worldwide. Emerging studies have suggested that its pathogenesis is distinct from nondiabetic renal diseases in many aspects. However, it still lacks a comprehensive understanding of the unique molecular...

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Autores principales: Zhou, Le-Ting, Zhang, Zhi-Jian, Cao, Jing-Yuan, Chen, Hanzhi, Zhu, Yu-Shan, Wu, Xi, Nawabi, Abdul Qadir, Liu, Xiaobin, Shan, Weiwei, Zhang, Yue, Zhang, Xi-Ran, Xue, Jing, Hu, Ling, Wang, Si-Si, Wang, Liang, Sun, Zhu-Xing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8162860/
https://www.ncbi.nlm.nih.gov/pubmed/34084458
http://dx.doi.org/10.1093/ckj/sfaa190
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author Zhou, Le-Ting
Zhang, Zhi-Jian
Cao, Jing-Yuan
Chen, Hanzhi
Zhu, Yu-Shan
Wu, Xi
Nawabi, Abdul Qadir
Liu, Xiaobin
Shan, Weiwei
Zhang, Yue
Zhang, Xi-Ran
Xue, Jing
Hu, Ling
Wang, Si-Si
Wang, Liang
Sun, Zhu-Xing
author_facet Zhou, Le-Ting
Zhang, Zhi-Jian
Cao, Jing-Yuan
Chen, Hanzhi
Zhu, Yu-Shan
Wu, Xi
Nawabi, Abdul Qadir
Liu, Xiaobin
Shan, Weiwei
Zhang, Yue
Zhang, Xi-Ran
Xue, Jing
Hu, Ling
Wang, Si-Si
Wang, Liang
Sun, Zhu-Xing
author_sort Zhou, Le-Ting
collection PubMed
description BACKGROUND/AIMS: Diabetic nephropathy (DN) is one of the main causes of end-stage kidney disease worldwide. Emerging studies have suggested that its pathogenesis is distinct from nondiabetic renal diseases in many aspects. However, it still lacks a comprehensive understanding of the unique molecular mechanism of DN. METHODS: A total of 255 Affymetrix U133 microarray datasets (Affymetrix, Santa Calra, CA, USA) of human glomerular and tubulointerstitial tissues were collected. The 22 215 Affymetrix identifiers shared by the Human Genome U133 Plus 2.0 and U133A Array were extracted to facilitate dataset pooling. Next, a linear model was constructed and the empirical Bayes method was used to select the differentially expressed genes (DEGs) of each kidney disease. Based on these DEG sets, the unique DEGs of DN were identified and further analyzed using gene ontology and pathway enrichment analysis. Finally, the protein–protein interaction networks (PINs) were constructed and hub genes were selected to further refine the results. RESULTS: A total of 129 and 1251 unique DEGs were identified in the diabetic glomerulus (upregulated n = 83 and downregulated n = 203) and the diabetic tubulointerstitium (upregulated n = 399 and downregulated n = 874), respectively. Enrichment analysis revealed that the DEGs in the diabetic glomerulus were significantly associated with the extracellular matrix, cell growth, regulation of blood coagulation, cholesterol homeostasis, intrinsic apoptotic signaling pathway and renal filtration cell differentiation. In the diabetic tubulointerstitium, the significantly enriched biological processes and pathways included metabolism, the advanced glycation end products–receptor for advanced glycation end products signaling pathway in diabetic complications, the epidermal growth factor receptor (EGFR) signaling pathway, the FoxO signaling pathway, autophagy and ferroptosis. By constructing PINs, several nodes, such as AGR2, CSNK2A1, EGFR and HSPD1, were identified as hub genes, which might play key roles in regulating the development of DN. CONCLUSIONS: Our study not only reveals the unique molecular mechanism of DN but also provides a valuable resource for biomarker and therapeutic target discovery. Some of our findings are promising and should be explored in future work.
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spelling pubmed-81628602021-06-02 The unique molecular mechanism of diabetic nephropathy: a bioinformatics analysis of over 250 microarray datasets Zhou, Le-Ting Zhang, Zhi-Jian Cao, Jing-Yuan Chen, Hanzhi Zhu, Yu-Shan Wu, Xi Nawabi, Abdul Qadir Liu, Xiaobin Shan, Weiwei Zhang, Yue Zhang, Xi-Ran Xue, Jing Hu, Ling Wang, Si-Si Wang, Liang Sun, Zhu-Xing Clin Kidney J Original Articles BACKGROUND/AIMS: Diabetic nephropathy (DN) is one of the main causes of end-stage kidney disease worldwide. Emerging studies have suggested that its pathogenesis is distinct from nondiabetic renal diseases in many aspects. However, it still lacks a comprehensive understanding of the unique molecular mechanism of DN. METHODS: A total of 255 Affymetrix U133 microarray datasets (Affymetrix, Santa Calra, CA, USA) of human glomerular and tubulointerstitial tissues were collected. The 22 215 Affymetrix identifiers shared by the Human Genome U133 Plus 2.0 and U133A Array were extracted to facilitate dataset pooling. Next, a linear model was constructed and the empirical Bayes method was used to select the differentially expressed genes (DEGs) of each kidney disease. Based on these DEG sets, the unique DEGs of DN were identified and further analyzed using gene ontology and pathway enrichment analysis. Finally, the protein–protein interaction networks (PINs) were constructed and hub genes were selected to further refine the results. RESULTS: A total of 129 and 1251 unique DEGs were identified in the diabetic glomerulus (upregulated n = 83 and downregulated n = 203) and the diabetic tubulointerstitium (upregulated n = 399 and downregulated n = 874), respectively. Enrichment analysis revealed that the DEGs in the diabetic glomerulus were significantly associated with the extracellular matrix, cell growth, regulation of blood coagulation, cholesterol homeostasis, intrinsic apoptotic signaling pathway and renal filtration cell differentiation. In the diabetic tubulointerstitium, the significantly enriched biological processes and pathways included metabolism, the advanced glycation end products–receptor for advanced glycation end products signaling pathway in diabetic complications, the epidermal growth factor receptor (EGFR) signaling pathway, the FoxO signaling pathway, autophagy and ferroptosis. By constructing PINs, several nodes, such as AGR2, CSNK2A1, EGFR and HSPD1, were identified as hub genes, which might play key roles in regulating the development of DN. CONCLUSIONS: Our study not only reveals the unique molecular mechanism of DN but also provides a valuable resource for biomarker and therapeutic target discovery. Some of our findings are promising and should be explored in future work. Oxford University Press 2021-03-18 /pmc/articles/PMC8162860/ /pubmed/34084458 http://dx.doi.org/10.1093/ckj/sfaa190 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of ERA-EDTA. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Original Articles
Zhou, Le-Ting
Zhang, Zhi-Jian
Cao, Jing-Yuan
Chen, Hanzhi
Zhu, Yu-Shan
Wu, Xi
Nawabi, Abdul Qadir
Liu, Xiaobin
Shan, Weiwei
Zhang, Yue
Zhang, Xi-Ran
Xue, Jing
Hu, Ling
Wang, Si-Si
Wang, Liang
Sun, Zhu-Xing
The unique molecular mechanism of diabetic nephropathy: a bioinformatics analysis of over 250 microarray datasets
title The unique molecular mechanism of diabetic nephropathy: a bioinformatics analysis of over 250 microarray datasets
title_full The unique molecular mechanism of diabetic nephropathy: a bioinformatics analysis of over 250 microarray datasets
title_fullStr The unique molecular mechanism of diabetic nephropathy: a bioinformatics analysis of over 250 microarray datasets
title_full_unstemmed The unique molecular mechanism of diabetic nephropathy: a bioinformatics analysis of over 250 microarray datasets
title_short The unique molecular mechanism of diabetic nephropathy: a bioinformatics analysis of over 250 microarray datasets
title_sort unique molecular mechanism of diabetic nephropathy: a bioinformatics analysis of over 250 microarray datasets
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8162860/
https://www.ncbi.nlm.nih.gov/pubmed/34084458
http://dx.doi.org/10.1093/ckj/sfaa190
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