Comprehensive analysis of diabetic nephropathy expression profile based on weighted gene co-expression network analysis algorithm
Abstract 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-expres...
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| Format: | Article |
| Language: | English |
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BMC
2021-07-01
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| Series: | BMC Nephrology |
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| Online Access: | https://doi.org/10.1186/s12882-021-02447-2 |
| _version_ | 1819121803365187584 |
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| author | Alieh Gholaminejad Mohammad Fathalipour Amir Roointan |
| author_facet | Alieh Gholaminejad Mohammad Fathalipour Amir Roointan |
| author_sort | Alieh Gholaminejad |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-22T06:42:22Z |
| format | Article |
| id | doaj.art-1f9d56384d254f15bc9961e90d78dd08 |
| institution | Directory Open Access Journal |
| issn | 1471-2369 |
| language | English |
| last_indexed | 2024-12-22T06:42:22Z |
| publishDate | 2021-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Nephrology |
| spelling | doaj.art-1f9d56384d254f15bc9961e90d78dd082022-12-21T18:35:23ZengBMCBMC Nephrology1471-23692021-07-0122111310.1186/s12882-021-02447-2Comprehensive analysis of diabetic nephropathy expression profile based on weighted gene co-expression network analysis algorithmAlieh Gholaminejad0Mohammad Fathalipour1Amir Roointan2Regenerative Medicine Research Center, Isfahan University of Medical SciencesDepartment of Pharmacology and Toxicology, Faculty of Pharmacy, Hormozgan University of Medical SciencesRegenerative Medicine Research Center, Isfahan University of Medical SciencesAbstract 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.https://doi.org/10.1186/s12882-021-02447-2Diabetic nephropathyWeighted gene co-expression networkTranscriptome analysisKey geneDrug target |
| spellingShingle | Alieh Gholaminejad Mohammad Fathalipour Amir Roointan Comprehensive analysis of diabetic nephropathy expression profile based on weighted gene co-expression network analysis algorithm BMC Nephrology Diabetic nephropathy Weighted gene co-expression network Transcriptome analysis Key gene Drug target |
| 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 | Diabetic nephropathy Weighted gene co-expression network Transcriptome analysis Key gene Drug target |
| url | https://doi.org/10.1186/s12882-021-02447-2 |
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