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1 "Minghui Zhao"
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Original Article
Type 1 Diabetes
Differential Profile of Plasma Circular RNAs in Type 1 Diabetes Mellitus
Yangyang Li, Ying Zhou, Minghui Zhao, Jing Zou, Yuxiao Zhu, Xuewen Yuan, Qianqi Liu, Hanqing Cai, Cong-Qiu Chu, Yu Liu
Diabetes Metab J. 2020;44(6):854-865.   Published online July 13, 2020
DOI: https://doi.org/10.4093/dmj.2019.0151
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  • 19 Web of Science
  • 19 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background

No currently available biomarkers or treatment regimens fully meet therapeutic needs of type 1 diabetes mellitus (T1DM). Circular RNA (circRNA) is a recently identified class of stable noncoding RNA that have been documented as potential biomarkers for various diseases. Our objective was to identify and analyze plasma circRNAs altered in T1DM.

Methods

We used microarray to screen differentially expressed plasma circRNAs in patients with new onset T1DM (n=3) and age-/gender-matched healthy controls (n=3). Then, we selected six candidates with highest fold-change and validated them by quantitative real-time polymerase chain reaction in independent human cohort samples (n=12). Bioinformatic tools were adopted to predict putative microRNAs (miRNAs) sponged by these validated circRNAs and their downstream messenger RNAs (mRNAs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to gain further insights into T1DM pathogenesis.

Results

We identified 68 differentially expressed circRNAs, with 61 and seven being up- and downregulated respectively. Four of the six selected candidates were successfully validated. Curations of their predicted interacting miRNAs revealed critical roles in inflammation and pathogenesis of autoimmune disorders. Functional relations were visualized by a circRNA-miRNA-mRNA network. GO and KEGG analyses identified multiple inflammation-related processes that could be potentially associated with T1DM pathogenesis, including cytokine-cytokine receptor interaction, inflammatory mediator regulation of transient receptor potential channels and leukocyte activation involved in immune response.

Conclusion

Our study report, for the first time, a profile of differentially expressed plasma circRNAs in new onset T1DM. Further in silico annotations and bioinformatics analyses supported future application of circRNAs as novel biomarkers of T1DM.

Citations

Citations to this article as recorded by  
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    International Journal of Biological Macromolecules.2024; 259: 128182.     CrossRef
  • Circular RNAs: Potential biomarkers and therapeutic targets for autoimmune diseases
    Ren-Jie Zhao, Wan-Ying Zhang, Xing-Xing Fan
    Heliyon.2024; 10(1): e23694.     CrossRef
  • Research progress of circular RNA molecules in aging and age-related diseases
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    Ageing Research Reviews.2023; 87: 101913.     CrossRef
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    Molecular Biotechnology.2023;[Epub]     CrossRef
  • Hsa_circRNA_405498 and hsa_circRNA_100033 Serve as Potential Biomarkers for Differential Diagnosis of Type 1 Diabetes
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  • Circular RNAs in Diabetic Nephropathy: Updates and Perspectives
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  • Differential Expression and Bioinformatics Analysis of Plasma-Derived Exosomal circRNA in Type 1 Diabetes Mellitus
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    Wenfeng Yin, Ziwei Zhang, Zilin Xiao, Xia Li, Shuoming Luo, Zhiguang Zhou
    Frontiers in Genetics.2022;[Epub]     CrossRef
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    Journal of Pharmacy and Pharmacology.2022;[Epub]     CrossRef
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  • Understanding Competitive Endogenous RNA Network Mechanism in Type 1 Diabetes Mellitus Using Computational and Bioinformatics Approaches
    Xuanzi Yi, Xu Cheng
    Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy.2021; Volume 14: 3865.     CrossRef

Diabetes Metab J : Diabetes & Metabolism Journal