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m(6)A regulator-mediated methylation modification patterns and tumor immune microenvironment in sarcoma

Background: Studies have shown that the RNA N(6)-methyladenosine (m(6)A) modification patterns are extensively involved in the development of multiple tumors. However, the association between the m(6)A regulator expression patterns and the sarcoma tumor immune microenvironment (TIME) remains unclear...

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Detalles Bibliográficos
Autores principales: Li, Zhehong, Wei, Junqiang, Zheng, Honghong, Gan, Xintian, Song, Mingze, Zhang, Yafang, Kong, Lingwei, Zhang, Chao, Yang, Jilong, Jin, Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8791212/
https://www.ncbi.nlm.nih.gov/pubmed/34979500
http://dx.doi.org/10.18632/aging.203807
Descripción
Sumario:Background: Studies have shown that the RNA N(6)-methyladenosine (m(6)A) modification patterns are extensively involved in the development of multiple tumors. However, the association between the m(6)A regulator expression patterns and the sarcoma tumor immune microenvironment (TIME) remains unclear. Methods: We systematically evaluated the m(6)A regulator expression patterns in patients with sarcoma based on known 23 m(6)A regulators. Different m(6)A regulator expression patterns were analyzed using gene set variation analysis and a single-sample gene set enrichment analysis algorithm. According to the results of consensus clustering, we classified the patients into four different clusters. Next, we subjected the four clusters to differential genetic analysis and established m(6)A-related differentially expressed genes (DEGs). We then calculated the m(6)A-related DEGs score and constructed the m(6)A-related gene signature, named m(6)A score. Finally, the 259 sarcoma samples were divided into high- and low-m(6)A score groups. We further evaluated the TIME landscape between the high- and low-m(6)A score groups. Results: We identified four different m(6)A modification clusters and found that each cluster had unique metabolic and immunological characteristics. Based on the 19 prognosis-related DEGs, we calculated the principal component analysis scores for each patient with sarcoma and classified them into high- and low-m(6)A score groups. Conclusions: The m(6)A regulator expression patterns and complexity of the sarcoma TIME landscape are closely related to each other. Systematic evaluation of m(6)A regulator expression patterns and m(6)A scores in patients with sarcoma will enhance our understanding of TIME characteristics.