Diagnostic gene signatures and aberrant pathway activation based on m6A methylation regulators in rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic autoimmune disease (AD) characterized by persistent synovial inflammation, bone erosion and progressive joint destruction. This research aimed to elucidate the potential roles and molecular mechanisms of N6-methyladenosine (m6A) methylation regulators in RA. An...

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Bibliographic Details
Published in:Frontiers in immunology 2022-12, Vol.13, p.1041284-1041284
Main Authors: Geng, Qishun, Cao, Xiaoxue, Fan, Danping, Gu, Xiaofeng, Zhang, Qian, Zhang, Mengxiao, Wang, Zheng, Deng, Tingting, Xiao, Cheng
Format: Article
Language:English
Subjects:
Adenosine
Arthritis, Rheumatoid - genetics
Autoimmune Diseases
cell cycle
Consensus
Humans
IGF2BP3
Immunology
M1 macrophages
Methylation
N6-methyladenosine
rheumatoid arthritis
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Summary:Rheumatoid arthritis (RA) is a chronic autoimmune disease (AD) characterized by persistent synovial inflammation, bone erosion and progressive joint destruction. This research aimed to elucidate the potential roles and molecular mechanisms of N6-methyladenosine (m6A) methylation regulators in RA. An array of tissues from 233 RA and 126 control samples was profiled and integrated for mRNA expression analysis. Following quality control and normalization, the cohort was split into training and validation sets. Five distinct machine learning feature selection methods were applied to the training set and validated in validation sets. Among the six models, the LASSO_λ-1se model not only performed better in the validation sets but also exhibited more stringent performance. Two m6A methylation regulators were identified as significant biomarkers by consensus feature selection from all four methods. IGF2BP3 and YTHDC2, which are differentially expressed in patients with RA and controls, were used to predict RA diagnosis with high accuracy. In addition, IGF2BP3 showed higher importance, which can regulate the G2/M transition to promote RA-FLS proliferation and affect M1 macrophage polarization. This consensus of multiple machine learning approaches identified two m6A methylation regulators that could distinguish patients with RA from controls. These m6A methylation regulators and their target genes may provide insight into RA pathogenesis and reveal novel disease regulators and putative drug targets.
ISSN:1664-3224
1664-3224
DOI:10.3389/fimmu.2022.1041284