Identification of key regulatory genes and their working mechanisms in type 1 diabetes

Abstract Background Type 1 diabetes (T1D) is an autoimmune disease characterized by the destruction of beta cells in pancreatic islets. Identification of the key genes involved in T1D progression and their mechanisms of action may contribute to a better understanding of T1D. Methods The microarray p...

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Main Authors: Hui Li (Author), Xiao Hu (Author), Jieqiong Li (Author), Wen Jiang (Author), Li Wang (Author), Xin Tan (Author)
Format: Book
Published: BMC, 2023-01-01T00:00:00Z.
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042 |a dc 
100 1 0 |a Hui Li  |e author 
700 1 0 |a Xiao Hu  |e author 
700 1 0 |a Jieqiong Li  |e author 
700 1 0 |a Wen Jiang  |e author 
700 1 0 |a Li Wang  |e author 
700 1 0 |a Xin Tan  |e author 
245 0 0 |a Identification of key regulatory genes and their working mechanisms in type 1 diabetes 
260 |b BMC,   |c 2023-01-01T00:00:00Z. 
500 |a 10.1186/s12920-023-01432-y 
500 |a 1755-8794 
520 |a Abstract Background Type 1 diabetes (T1D) is an autoimmune disease characterized by the destruction of beta cells in pancreatic islets. Identification of the key genes involved in T1D progression and their mechanisms of action may contribute to a better understanding of T1D. Methods The microarray profile of T1D-related gene expression was searched using the Gene Expression Omnibus (GEO) database. Then, the expression data of two messenger RNAs (mRNAs) were integrated for Weighted Gene Co-Expression Network Analysis (WGCNA) to generate candidate genes related to T1D. In parallel, T1D microRNA (miRNA) data were analyzed to screen for possible regulatory miRNAs and their target genes. An miRNA-mRNA regulatory network was then established to predict the key regulatory genes and their mechanisms. Results A total of 24 modules (i.e., clusters/communities) were selected using WGCNA analysis, in which three modules were significantly associated with T1D. Further correlation analysis of the gene module revealed 926 differentially expressed genes (DEGs), of which 327 genes were correlated with T1D. Analysis of the miRNA microarray showed that 13 miRNAs had significant expression differences in T1D. An miRNA-mRNA network was established based on the prediction of miRNA target genes and the combined analysis of mRNA, in which the target genes of two miRNAs were found in T1D correlated genes. Conclusion An miRNA-mRNA network for T1D was established, based on which 2 miRNAs and 12 mRNAs were screened, suggesting that they may play key regulatory roles in the initiation and development of T1D. 
546 |a EN 
690 |a Type 1 diabetes 
690 |a WGCNA 
690 |a GEO 
690 |a Differentially expressed genes 
690 |a Internal medicine 
690 |a RC31-1245 
690 |a Genetics 
690 |a QH426-470 
655 7 |a article  |2 local 
786 0 |n BMC Medical Genomics, Vol 16, Iss 1, Pp 1-11 (2023) 
787 0 |n https://doi.org/10.1186/s12920-023-01432-y 
787 0 |n https://doaj.org/toc/1755-8794 
856 4 1 |u https://doaj.org/article/004032fd6b0a4ecba7ee7ca3300df1d6  |z Connect to this object online.