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Comprehensive analysis of diabetic nephropathy expression profile based on weighted gene co-expression network analysis algorithm

BACKGROUND: Diabetic nephropathy (DN) is the major complication of diabetes mellitus, and leading cause of end-stage renal disease. The underlying molecular mechanism of DN is not yet completely clear. The aim of this study was to analyze a DN microarray dataset using weighted gene co-expression net...

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Autores principales: Gholaminejad, Alieh, Fathalipour, Mohammad, Roointan, Amir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8252307/
https://www.ncbi.nlm.nih.gov/pubmed/34215202
http://dx.doi.org/10.1186/s12882-021-02447-2
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author Gholaminejad, Alieh
Fathalipour, Mohammad
Roointan, Amir
author_facet Gholaminejad, Alieh
Fathalipour, Mohammad
Roointan, Amir
author_sort Gholaminejad, Alieh
collection PubMed
description BACKGROUND: Diabetic nephropathy (DN) is the major complication of diabetes mellitus, and leading cause of end-stage renal disease. The underlying molecular mechanism of DN is not yet completely clear. The aim of this study was to analyze a DN microarray dataset using weighted gene co-expression network analysis (WGCNA) algorithm for better understanding of DN pathogenesis and exploring key genes in the disease progression. METHODS: The identified differentially expressed genes (DEGs) in DN dataset GSE47183 were introduced to WGCNA algorithm to construct co-expression modules. STRING database was used for construction of Protein-protein interaction (PPI) networks of the genes in all modules and the hub genes were identified considering both the degree centrality in the PPI networks and the ranked lists of weighted networks. Gene ontology and Reactome pathway enrichment analyses were performed on each module to understand their involvement in the biological processes and pathways. Following validation of the hub genes in another DN dataset (GSE96804), their up-stream regulators, including microRNAs and transcription factors were predicted and a regulatory network comprising of all these molecules was constructed. RESULTS: After normalization and analysis of the dataset, 2475 significant DEGs were identified and clustered into six different co-expression modules by WGCNA algorithm. Then, DEGs of each module were subjected to functional enrichment analyses and PPI network constructions. Metabolic processes, cell cycle control, and apoptosis were among the top enriched terms. In the next step, 23 hub genes were identified among the modules in genes and five of them, including FN1, SLC2A2, FABP1, EHHADH and PIPOX were validated in another DN dataset. In the regulatory network, FN1 was the most affected hub gene and mir-27a and REAL were recognized as two main upstream-regulators of the hub genes. CONCLUSIONS: The identified hub genes from the hearts of co-expression modules could widen our understanding of the DN development and might be of targets of future investigations, exploring their therapeutic potentials for treatment of this complicated disease.
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spelling pubmed-82523072021-07-06 Comprehensive analysis of diabetic nephropathy expression profile based on weighted gene co-expression network analysis algorithm Gholaminejad, Alieh Fathalipour, Mohammad Roointan, Amir BMC Nephrol Research BACKGROUND: Diabetic nephropathy (DN) is the major complication of diabetes mellitus, and leading cause of end-stage renal disease. The underlying molecular mechanism of DN is not yet completely clear. The aim of this study was to analyze a DN microarray dataset using weighted gene co-expression network analysis (WGCNA) algorithm for better understanding of DN pathogenesis and exploring key genes in the disease progression. METHODS: The identified differentially expressed genes (DEGs) in DN dataset GSE47183 were introduced to WGCNA algorithm to construct co-expression modules. STRING database was used for construction of Protein-protein interaction (PPI) networks of the genes in all modules and the hub genes were identified considering both the degree centrality in the PPI networks and the ranked lists of weighted networks. Gene ontology and Reactome pathway enrichment analyses were performed on each module to understand their involvement in the biological processes and pathways. Following validation of the hub genes in another DN dataset (GSE96804), their up-stream regulators, including microRNAs and transcription factors were predicted and a regulatory network comprising of all these molecules was constructed. RESULTS: After normalization and analysis of the dataset, 2475 significant DEGs were identified and clustered into six different co-expression modules by WGCNA algorithm. Then, DEGs of each module were subjected to functional enrichment analyses and PPI network constructions. Metabolic processes, cell cycle control, and apoptosis were among the top enriched terms. In the next step, 23 hub genes were identified among the modules in genes and five of them, including FN1, SLC2A2, FABP1, EHHADH and PIPOX were validated in another DN dataset. In the regulatory network, FN1 was the most affected hub gene and mir-27a and REAL were recognized as two main upstream-regulators of the hub genes. CONCLUSIONS: The identified hub genes from the hearts of co-expression modules could widen our understanding of the DN development and might be of targets of future investigations, exploring their therapeutic potentials for treatment of this complicated disease. BioMed Central 2021-07-02 /pmc/articles/PMC8252307/ /pubmed/34215202 http://dx.doi.org/10.1186/s12882-021-02447-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Gholaminejad, Alieh
Fathalipour, Mohammad
Roointan, Amir
Comprehensive analysis of diabetic nephropathy expression profile based on weighted gene co-expression network analysis algorithm
title Comprehensive analysis of diabetic nephropathy expression profile based on weighted gene co-expression network analysis algorithm
title_full Comprehensive analysis of diabetic nephropathy expression profile based on weighted gene co-expression network analysis algorithm
title_fullStr Comprehensive analysis of diabetic nephropathy expression profile based on weighted gene co-expression network analysis algorithm
title_full_unstemmed Comprehensive analysis of diabetic nephropathy expression profile based on weighted gene co-expression network analysis algorithm
title_short Comprehensive analysis of diabetic nephropathy expression profile based on weighted gene co-expression network analysis algorithm
title_sort comprehensive analysis of diabetic nephropathy expression profile based on weighted gene co-expression network analysis algorithm
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8252307/
https://www.ncbi.nlm.nih.gov/pubmed/34215202
http://dx.doi.org/10.1186/s12882-021-02447-2
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