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Weighted gene co-expression network analysis identifies FCER1G as a key gene associated with diabetic kidney disease

BACKGROUND: Diabetic kidney disease (DKD) is the primary cause of end-stage renal disease. However, the pathogenesis of DKD remains unclarified, and there is an urgent need for improved treatments. Recently, many crucial genes closely linked to the molecular mechanism underlying various diseases wer...

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Autores principales: Liu, Shanshan, Wang, Cuili, Yang, Huiying, Zhu, Tingting, Jiang, Hong, Chen, Jianghua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7723642/
https://www.ncbi.nlm.nih.gov/pubmed/33313172
http://dx.doi.org/10.21037/atm-20-1087
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author Liu, Shanshan
Wang, Cuili
Yang, Huiying
Zhu, Tingting
Jiang, Hong
Chen, Jianghua
author_facet Liu, Shanshan
Wang, Cuili
Yang, Huiying
Zhu, Tingting
Jiang, Hong
Chen, Jianghua
author_sort Liu, Shanshan
collection PubMed
description BACKGROUND: Diabetic kidney disease (DKD) is the primary cause of end-stage renal disease. However, the pathogenesis of DKD remains unclarified, and there is an urgent need for improved treatments. Recently, many crucial genes closely linked to the molecular mechanism underlying various diseases were discovered using weighted gene co-expression network analysis. METHODS: We used a gene expression omnibus series dataset GSE104948 with 12 renal glomerular DKD tissue samples and 18 control samples obtained from the gene expression omnibus database and performed weighted gene co-expression network analysis. After obtaining the trait-related modules, gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses of the modules were conducted and the key gene associated with DKD was selected from the top two most significant gene ontology terms using the maximal clique centrality method. Finally, we verified the key gene using protein-protein interaction analysis, additional datasets, and explored the relationship between the key gene and DKD renal function using the Nephroseq v5 online database. RESULTS: Among the 10 gene co-expression modules identified, the darkorange2 and red modules were highly related to DKD and the normal biological process, respectively. Majority of the genes in the darkorange2 module were related to immune and inflammatory responses, and potentially related to the progression of DKD due to their abnormal up-regulation. After performing sub-network analysis of the genes extracted from the top two most significant gene ontology terms and calculating the maximal clique centrality values of each gene, FCER1G, located at the center of the protein-protein interaction network, was identified as a key gene related to DKD. Furthermore, gene expression omnibus validation in additional datasets also showed that FCER1G was overexpressed in DKD compared with normal tissues. Finally, Pearson’s correlation analysis between the expression of FCER1G and DKD renal function revealed that the abnormal upregulation of FCER1G was related to diabetic glomerular lesions. CONCLUSIONS: Our study demonstrated for the first time that FCER1G is a crucial gene associated with the pathogenesis of DKD.
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spelling pubmed-77236422020-12-10 Weighted gene co-expression network analysis identifies FCER1G as a key gene associated with diabetic kidney disease Liu, Shanshan Wang, Cuili Yang, Huiying Zhu, Tingting Jiang, Hong Chen, Jianghua Ann Transl Med Original Article BACKGROUND: Diabetic kidney disease (DKD) is the primary cause of end-stage renal disease. However, the pathogenesis of DKD remains unclarified, and there is an urgent need for improved treatments. Recently, many crucial genes closely linked to the molecular mechanism underlying various diseases were discovered using weighted gene co-expression network analysis. METHODS: We used a gene expression omnibus series dataset GSE104948 with 12 renal glomerular DKD tissue samples and 18 control samples obtained from the gene expression omnibus database and performed weighted gene co-expression network analysis. After obtaining the trait-related modules, gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses of the modules were conducted and the key gene associated with DKD was selected from the top two most significant gene ontology terms using the maximal clique centrality method. Finally, we verified the key gene using protein-protein interaction analysis, additional datasets, and explored the relationship between the key gene and DKD renal function using the Nephroseq v5 online database. RESULTS: Among the 10 gene co-expression modules identified, the darkorange2 and red modules were highly related to DKD and the normal biological process, respectively. Majority of the genes in the darkorange2 module were related to immune and inflammatory responses, and potentially related to the progression of DKD due to their abnormal up-regulation. After performing sub-network analysis of the genes extracted from the top two most significant gene ontology terms and calculating the maximal clique centrality values of each gene, FCER1G, located at the center of the protein-protein interaction network, was identified as a key gene related to DKD. Furthermore, gene expression omnibus validation in additional datasets also showed that FCER1G was overexpressed in DKD compared with normal tissues. Finally, Pearson’s correlation analysis between the expression of FCER1G and DKD renal function revealed that the abnormal upregulation of FCER1G was related to diabetic glomerular lesions. CONCLUSIONS: Our study demonstrated for the first time that FCER1G is a crucial gene associated with the pathogenesis of DKD. AME Publishing Company 2020-11 /pmc/articles/PMC7723642/ /pubmed/33313172 http://dx.doi.org/10.21037/atm-20-1087 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
Liu, Shanshan
Wang, Cuili
Yang, Huiying
Zhu, Tingting
Jiang, Hong
Chen, Jianghua
Weighted gene co-expression network analysis identifies FCER1G as a key gene associated with diabetic kidney disease
title Weighted gene co-expression network analysis identifies FCER1G as a key gene associated with diabetic kidney disease
title_full Weighted gene co-expression network analysis identifies FCER1G as a key gene associated with diabetic kidney disease
title_fullStr Weighted gene co-expression network analysis identifies FCER1G as a key gene associated with diabetic kidney disease
title_full_unstemmed Weighted gene co-expression network analysis identifies FCER1G as a key gene associated with diabetic kidney disease
title_short Weighted gene co-expression network analysis identifies FCER1G as a key gene associated with diabetic kidney disease
title_sort weighted gene co-expression network analysis identifies fcer1g as a key gene associated with diabetic kidney disease
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7723642/
https://www.ncbi.nlm.nih.gov/pubmed/33313172
http://dx.doi.org/10.21037/atm-20-1087
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