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Identification of key regulatory genes and their working mechanisms in type 1 diabetes
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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9843847/ https://www.ncbi.nlm.nih.gov/pubmed/36650594 http://dx.doi.org/10.1186/s12920-023-01432-y |
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author | Li, Hui Hu, Xiao Li, Jieqiong Jiang, Wen Wang, Li Tan, Xin |
author_facet | Li, Hui Hu, Xiao Li, Jieqiong Jiang, Wen Wang, Li Tan, Xin |
author_sort | Li, Hui |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-023-01432-y. |
format | Online Article Text |
id | pubmed-9843847 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-98438472023-01-18 Identification of key regulatory genes and their working mechanisms in type 1 diabetes Li, Hui Hu, Xiao Li, Jieqiong Jiang, Wen Wang, Li Tan, Xin BMC Med Genomics Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12920-023-01432-y. BioMed Central 2023-01-17 /pmc/articles/PMC9843847/ /pubmed/36650594 http://dx.doi.org/10.1186/s12920-023-01432-y Text en © The Author(s) 2023 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 Li, Hui Hu, Xiao Li, Jieqiong Jiang, Wen Wang, Li Tan, Xin Identification of key regulatory genes and their working mechanisms in type 1 diabetes |
title | Identification of key regulatory genes and their working mechanisms in type 1 diabetes |
title_full | Identification of key regulatory genes and their working mechanisms in type 1 diabetes |
title_fullStr | Identification of key regulatory genes and their working mechanisms in type 1 diabetes |
title_full_unstemmed | Identification of key regulatory genes and their working mechanisms in type 1 diabetes |
title_short | Identification of key regulatory genes and their working mechanisms in type 1 diabetes |
title_sort | identification of key regulatory genes and their working mechanisms in type 1 diabetes |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9843847/ https://www.ncbi.nlm.nih.gov/pubmed/36650594 http://dx.doi.org/10.1186/s12920-023-01432-y |
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