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Crucial genes associated with diabetic nephropathy explored by microarray analysis

View Article: PubMed Central - PubMed

ABSTRACT

Background: This study sought to investigate crucial genes correlated with diabetic nephropathy (DN), and their potential functions, which might contribute to a better understanding of DN pathogenesis.

Methods: The microarray dataset GSE1009 was downloaded from Gene Expression Omnibus, including 3 diabetic glomeruli samples and 3 healthy glomeruli samples. The differentially expressed genes (DEGs) were identified by LIMMA package. Their potential functions were then analyzed by the GO and KEGG pathway enrichment analyses using the DAVID database. Furthermore, miRNAs and transcription factors (TFs) regulating DEGs were predicted by the GeneCoDis tool, and miRNA-DEG-TF regulatory network was visualized by Cytoscape. Additionally, the expression of DEGs was validated using another microarray dataset GSE30528.

Results: Totally, 14 up-regulated DEGs and 430 down-regulated ones were identified. Some DEGs (e.g. MTSS1, CALD1 and ACTN4) were markedly relative to cytoskeleton organization. Besides, some other ones were correlated with arrhythmogenic right ventricular cardiomyopathy (e.g. ACTN4, CTNNA1 and ITGB5), as well as complement and coagulation cascades (e.g. C1R and C1S). Furthermore, a series of miRNAs and TFs modulating DEGs were identified. The transcription factor LEF1 regulated the majority of DEGs, such as ITGB5, CALD1 and C1S. Hsa-miR-33a modulated 28 genes, such as C1S. Additionally, 143 DEGs (one upregulated gene and 142 downregulated genes) were also differentially expressed in another dataset GSE30528.

Conclusions: The genes involved in cytoskeleton organization, cardiomyopathy, as well as complement and coagulation cascades may be closely implicated in the progression of DN, via the regulation of miRNAs and TFs.

Electronic supplementary material: The online version of this article (doi:10.1186/s12882-016-0343-2) contains supplementary material, which is available to authorized users.

No MeSH data available.


The Venn diagram showing the overlapped differentially expressed genes in the two datasets GSE30528 and GSE1009. “GSE30528-UP” represents the upregulated genes in the dataset GSE30528; “GSE30528-DOWN” represents the downregulated genes in the dataset GSE30528; “GSE1009-UP” represents the upregulated genes in the dataset GSE1009; and “GSE1009-DOWN” represents the downregulated genes in the dataset GSE1009. Arabic numerals in the diagram represent the numbers of the overlapped genes
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Fig3: The Venn diagram showing the overlapped differentially expressed genes in the two datasets GSE30528 and GSE1009. “GSE30528-UP” represents the upregulated genes in the dataset GSE30528; “GSE30528-DOWN” represents the downregulated genes in the dataset GSE30528; “GSE1009-UP” represents the upregulated genes in the dataset GSE1009; and “GSE1009-DOWN” represents the downregulated genes in the dataset GSE1009. Arabic numerals in the diagram represent the numbers of the overlapped genes

Mentions: To validate the expression of the identified DEGs in the dataset GSE1009, another dataset GSE30528 was used. A total of 635 DEGs were identified in GSE30528. Among them, 143 DEGs, including one upregulated gene (TRIM16) and 142 downregulated genes (e.g. MTSS1, ACTN4 and ITGB5), were also differentially expressed in the dataset GSE1009 (Fig. 3), indicating that the expression of the 143 DEGs in GSE1009 identified above were validated by the dataset GSE30528.Fig. 3


Crucial genes associated with diabetic nephropathy explored by microarray analysis
The Venn diagram showing the overlapped differentially expressed genes in the two datasets GSE30528 and GSE1009. “GSE30528-UP” represents the upregulated genes in the dataset GSE30528; “GSE30528-DOWN” represents the downregulated genes in the dataset GSE30528; “GSE1009-UP” represents the upregulated genes in the dataset GSE1009; and “GSE1009-DOWN” represents the downregulated genes in the dataset GSE1009. Arabic numerals in the diagram represent the numbers of the overlapped genes
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5016939&req=5

Fig3: The Venn diagram showing the overlapped differentially expressed genes in the two datasets GSE30528 and GSE1009. “GSE30528-UP” represents the upregulated genes in the dataset GSE30528; “GSE30528-DOWN” represents the downregulated genes in the dataset GSE30528; “GSE1009-UP” represents the upregulated genes in the dataset GSE1009; and “GSE1009-DOWN” represents the downregulated genes in the dataset GSE1009. Arabic numerals in the diagram represent the numbers of the overlapped genes
Mentions: To validate the expression of the identified DEGs in the dataset GSE1009, another dataset GSE30528 was used. A total of 635 DEGs were identified in GSE30528. Among them, 143 DEGs, including one upregulated gene (TRIM16) and 142 downregulated genes (e.g. MTSS1, ACTN4 and ITGB5), were also differentially expressed in the dataset GSE1009 (Fig. 3), indicating that the expression of the 143 DEGs in GSE1009 identified above were validated by the dataset GSE30528.Fig. 3

View Article: PubMed Central - PubMed

ABSTRACT

Background: This study sought to investigate crucial genes correlated with diabetic nephropathy (DN), and their potential functions, which might contribute to a better understanding of DN pathogenesis.

Methods: The microarray dataset GSE1009 was downloaded from Gene Expression Omnibus, including 3 diabetic glomeruli samples and 3 healthy glomeruli samples. The differentially expressed genes (DEGs) were identified by LIMMA package. Their potential functions were then analyzed by the GO and KEGG pathway enrichment analyses using the DAVID database. Furthermore, miRNAs and transcription factors (TFs) regulating DEGs were predicted by the GeneCoDis tool, and miRNA-DEG-TF regulatory network was visualized by Cytoscape. Additionally, the expression of DEGs was validated using another microarray dataset GSE30528.

Results: Totally, 14 up-regulated DEGs and 430 down-regulated ones were identified. Some DEGs (e.g. MTSS1, CALD1 and ACTN4) were markedly relative to cytoskeleton organization. Besides, some other ones were correlated with arrhythmogenic right ventricular cardiomyopathy (e.g. ACTN4, CTNNA1 and ITGB5), as well as complement and coagulation cascades (e.g. C1R and C1S). Furthermore, a series of miRNAs and TFs modulating DEGs were identified. The transcription factor LEF1 regulated the majority of DEGs, such as ITGB5, CALD1 and C1S. Hsa-miR-33a modulated 28 genes, such as C1S. Additionally, 143 DEGs (one upregulated gene and 142 downregulated genes) were also differentially expressed in another dataset GSE30528.

Conclusions: The genes involved in cytoskeleton organization, cardiomyopathy, as well as complement and coagulation cascades may be closely implicated in the progression of DN, via the regulation of miRNAs and TFs.

Electronic supplementary material: The online version of this article (doi:10.1186/s12882-016-0343-2) contains supplementary material, which is available to authorized users.

No MeSH data available.