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Understanding Competitive Endogenous RNA Network Mechanism in Type 1 Diabetes Mellitus Using Computational and Bioinformatics Approaches

BACKGROUND: Type 1 diabetes mellitus (T1DM), an autoimmune disease with a genetic tendency, has an increasing prevalence. Long non-coding RNA (lncRNA) and circular RNA (circRNA) are receiving increasing attention in disease pathogenesis. However, their roles in T1DM are poorly understood. The presen...

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Autores principales: Yi, Xuanzi, Cheng, Xu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8436179/
https://www.ncbi.nlm.nih.gov/pubmed/34526791
http://dx.doi.org/10.2147/DMSO.S315488
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author Yi, Xuanzi
Cheng, Xu
author_facet Yi, Xuanzi
Cheng, Xu
author_sort Yi, Xuanzi
collection PubMed
description BACKGROUND: Type 1 diabetes mellitus (T1DM), an autoimmune disease with a genetic tendency, has an increasing prevalence. Long non-coding RNA (lncRNA) and circular RNA (circRNA) are receiving increasing attention in disease pathogenesis. However, their roles in T1DM are poorly understood. The present study aimed at identifying signature lncRNAs and circRNAs and investigating their roles in T1DM using the competing endogenous RNA (ceRNA) network analysis. METHODS: The T1DM expression profile was downloaded from Gene Expression Omnibus (GEO) database to identify the differentially expressed circRNAs, lncRNAs, and mRNAs. The biological functions of these differentially expressed circRNAs, lncRNAs, and mRNAs were analyzed by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Targeting relationships of circRNA-miRNA, lncRNA-miRNA, and miRNA-mRNA were predicted, and the circRNA-lncRNA-miRNA-mRNA ceRNA regulatory network was established. Finally, qRT-PCR was applied to identify the effect of hsa_circ_0002202 inhibition on the IFN-I induced macrophage inflammation. RESULTS: A total of 178 circRNAs, 404 lncRNAs, and 73 mRNAs were identified to be abnormally expressed in T1DM samples. Functional enrichment analysis results indicated that the differentially expressed genes were mainly enriched in extracellular matrix components and macrophage activation. CeRNA regulatory network showed that circRNAs and lncRNAs regulate mRNAs through integrate multiple miRNAs. In addition, in vitro experiments showed that hsa_circ_0002202 inhibition suppressed the type I interferon (IFN-I)-induced macrophage inflammation. CONCLUSION: In the present study, the circRNA-lncRNA-miRNA-mRNA ceRNA regulatory network in T1DM was established for the first time. We also found that hsa_circ_0002202 inhibition suppressed the IFN-I-induced macrophage inflammation. Our study may lay a foundation for future studies on the ceRNA regulatory network in T1DM.
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spelling pubmed-84361792021-09-14 Understanding Competitive Endogenous RNA Network Mechanism in Type 1 Diabetes Mellitus Using Computational and Bioinformatics Approaches Yi, Xuanzi Cheng, Xu Diabetes Metab Syndr Obes Original Research BACKGROUND: Type 1 diabetes mellitus (T1DM), an autoimmune disease with a genetic tendency, has an increasing prevalence. Long non-coding RNA (lncRNA) and circular RNA (circRNA) are receiving increasing attention in disease pathogenesis. However, their roles in T1DM are poorly understood. The present study aimed at identifying signature lncRNAs and circRNAs and investigating their roles in T1DM using the competing endogenous RNA (ceRNA) network analysis. METHODS: The T1DM expression profile was downloaded from Gene Expression Omnibus (GEO) database to identify the differentially expressed circRNAs, lncRNAs, and mRNAs. The biological functions of these differentially expressed circRNAs, lncRNAs, and mRNAs were analyzed by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Targeting relationships of circRNA-miRNA, lncRNA-miRNA, and miRNA-mRNA were predicted, and the circRNA-lncRNA-miRNA-mRNA ceRNA regulatory network was established. Finally, qRT-PCR was applied to identify the effect of hsa_circ_0002202 inhibition on the IFN-I induced macrophage inflammation. RESULTS: A total of 178 circRNAs, 404 lncRNAs, and 73 mRNAs were identified to be abnormally expressed in T1DM samples. Functional enrichment analysis results indicated that the differentially expressed genes were mainly enriched in extracellular matrix components and macrophage activation. CeRNA regulatory network showed that circRNAs and lncRNAs regulate mRNAs through integrate multiple miRNAs. In addition, in vitro experiments showed that hsa_circ_0002202 inhibition suppressed the type I interferon (IFN-I)-induced macrophage inflammation. CONCLUSION: In the present study, the circRNA-lncRNA-miRNA-mRNA ceRNA regulatory network in T1DM was established for the first time. We also found that hsa_circ_0002202 inhibition suppressed the IFN-I-induced macrophage inflammation. Our study may lay a foundation for future studies on the ceRNA regulatory network in T1DM. Dove 2021-09-08 /pmc/articles/PMC8436179/ /pubmed/34526791 http://dx.doi.org/10.2147/DMSO.S315488 Text en © 2021 Yi and Cheng. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Yi, Xuanzi
Cheng, Xu
Understanding Competitive Endogenous RNA Network Mechanism in Type 1 Diabetes Mellitus Using Computational and Bioinformatics Approaches
title Understanding Competitive Endogenous RNA Network Mechanism in Type 1 Diabetes Mellitus Using Computational and Bioinformatics Approaches
title_full Understanding Competitive Endogenous RNA Network Mechanism in Type 1 Diabetes Mellitus Using Computational and Bioinformatics Approaches
title_fullStr Understanding Competitive Endogenous RNA Network Mechanism in Type 1 Diabetes Mellitus Using Computational and Bioinformatics Approaches
title_full_unstemmed Understanding Competitive Endogenous RNA Network Mechanism in Type 1 Diabetes Mellitus Using Computational and Bioinformatics Approaches
title_short Understanding Competitive Endogenous RNA Network Mechanism in Type 1 Diabetes Mellitus Using Computational and Bioinformatics Approaches
title_sort understanding competitive endogenous rna network mechanism in type 1 diabetes mellitus using computational and bioinformatics approaches
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8436179/
https://www.ncbi.nlm.nih.gov/pubmed/34526791
http://dx.doi.org/10.2147/DMSO.S315488
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