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Landscape analysis of m6A modification regulators related biological functions and immune characteristics in myasthenia gravis

Overview of attention for article published in Journal of Translational Medicine, March 2023
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Title
Landscape analysis of m6A modification regulators related biological functions and immune characteristics in myasthenia gravis
Published in
Journal of Translational Medicine, March 2023
DOI 10.1186/s12967-023-03947-5
Pubmed ID
Authors

Shuang Li, Hui Liu, Zhe Ruan, Rongjing Guo, Chao Sun, Yonglan Tang, Xiaoxi Huang, Ting Gao, Sijia Hao, Huanhuan Li, Na Song, Yue Su, Fan Ning, Zhuyi Li, Ting Chang

Abstract

N6-methyladenosine (m6A) modification has been recognized to play fundamental roles in the development of autoimmune diseases. However, the implication of m6A modification in myasthenia gravis (MG) remains largely unknown. Thus, we aimed to systematically explore the potential functions and related immune characteristics of m6A regulators in MG. The GSE85452 dataset with MG and healthy samples was downloaded from Gene Expression Omnibus (GEO) database. m6A modification regulators were manually curated. The targets of m6A regulators were obtained from m6A2Target database. The differential expressed m6A regulators in GSE85452 dataset were identified by "limma" package and were validated by RT-PCR. Function enrichment analysis of dysregulated m6A regulators was performed using "clusterProfiler" package. Correlation analysis was applied for analyzing the relationships between m6A regulators and immune characteristics. Unsupervised clustering analysis was used to identify distinct m6A modification subtypes. The differences between subtypes were analyzed, including the expression level of all genes and the enrichment degree of immune characteristics. Weighted gene co-expression network analysis (WGCNA) was conducted to obtain modules associated with m6A modification subtypes. We found that CBLL1, RBM15 and YTHDF1 were upregulated in MG samples of GSE85452 dataset, and the results were verified by RT-PCR in blood samples from19 MG patients and 19 controls. The targeted genes common modified by CBLL1, RBM15, and YTHDF1 were mainly enriched in histone modification and Wnt signaling pathway. Correlation analysis showed that three dysregulated m6A regulators were closely associated with immune characteristics. Among them, RBM15 possessed the strongest correlation with immune characteristics, including CD56dim natural killer cell (r = 0.77, P = 0.0023), T follicular helper cell (r = - 0.86, P = 0.0002), Interferon Receptor (r = 0.78, P = 0.0017), and HLA-DOA (r = 0.64, P = 0.0200). Further two distinct m6A modification patterns mediated by three dysregulated m6A regulators was identified. Bioinformatics analysis found that there were 3029 differentially expressed genes and different immune characteristics between two m6A modification patterns. Finally, WGCNA analysis obtained a total of 12 modules and yellow module was the most positively correlated to subtype-2. Our findings suggested that m6A RNA modification had an important effect on immunity molecular mechanism of MG and provided a new perspective into understanding the pathogenesis of MG.

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Mendeley readers

Mendeley readers

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Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 11%
Researcher 1 11%
Student > Doctoral Student 1 11%
Student > Master 1 11%
Unknown 5 56%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 1 11%
Neuroscience 1 11%
Medicine and Dentistry 1 11%
Engineering 1 11%
Unknown 5 56%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 March 2023.
All research outputs
#19,968,204
of 25,403,829 outputs
Outputs from Journal of Translational Medicine
#3,205
of 4,645 outputs
Outputs of similar age
#298,987
of 422,387 outputs
Outputs of similar age from Journal of Translational Medicine
#114
of 146 outputs
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