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Characterization of immune microenvironment infiltration and m(6)A regulator-mediated RNA methylation modification patterns in osteoarthritis

BACKGROUND: Few studies have been reported the potential role of N6-methyladenosine (m(6)A) modification in osteoarthritis (OA). We investigated the patterns of m(6)A modification in the immune microenvironment of OA. METHODS: We evaluated the m(6)A modification patterns based on 22 m(6)A regulators...

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Detalles Bibliográficos
Autores principales: Ouyang, Yulong, Tu, Yuanqing, Chen, Shuilin, Min, Huan, Wen, Zhexu, Zheng, Guihao, Wan, Ting, Fan, Hao, Yang, Wenzhao, Sun, Guicai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9728527/
https://www.ncbi.nlm.nih.gov/pubmed/36505479
http://dx.doi.org/10.3389/fimmu.2022.1018701
Descripción
Sumario:BACKGROUND: Few studies have been reported the potential role of N6-methyladenosine (m(6)A) modification in osteoarthritis (OA). We investigated the patterns of m(6)A modification in the immune microenvironment of OA. METHODS: We evaluated the m(6)A modification patterns based on 22 m(6)A regulators in 139 OA samples and systematically associated these modification patterns with immune cell infiltration characteristics. The function of m(6)A phenotype-related differentially expressed genes (DEGs) was investigated using gene enrichment analysis. An m(6)A score model was constructed using principal component analysis (PCA), and an OA prediction model was established based on the key m(6)A regulators. We used real-time PCR analysis to detect the changes of gene expression in the cell model of OA. RESULTS: Healthy and OA samples showed significant differences in the expression of m(6)A regulators. Nine key m(6)A regulators, two m(6)A modification patterns, m(6)A-related genes and two gene clusters were identified. Some m(6)A regulators had a strong correlation with each other. Gene clusters and m(6)A clusters have high similarity, and cluster A corresponds to a high m(6)A score. Immunocytes infiltration differed significantly between the two clusters, with the m(6)A cluster B and gene cluster B having more types of infiltrating immunocytes than cluster A. The predictive model can also predict the progression of OA through m(6)A regulators expression. The results of real-time PCR analysis showed that the gene expression in the cell model of OA is similar to that of the m(6)A cluster B. CONCLUSIONS: Our study reveals for the first time the potential regulatory mechanism of m(6)A modification in the immune microenvironment of OA. This study also sheds new light on the pathogenesis of OA.