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. 2021 Nov 25;9:725764. doi: 10.3389/fcell.2021.725764

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Copyright © 2021 Liu, Li, Wu, Xu, Chen, Zhang, Huang, Zhao and Wang.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Unsupervised clustering of m6A genes in low-grade glioma samples. (A) Interaction between m6A genes. The size of the circle indicates the impact of each gene on survival prediction, and the larger the expression, the more relevant the prognosis. In the circle the green dots in the circle indicate prognostic protective factors, and the black dots in the circle indicate prognostic risk factors. The lines connecting genes show their interactions. The negative correlations are marked in blue and positive correlations in red. Gene clusters ABC are marked in blue, respectively, color, red and brown. (B) Consistent clustering of m6A genes. (C) Kaplan-Meier curve showing significant survival differences in two m6A regulators clusters. (D) GSVA enrichment analysis, showing the biological pathways with different m6A regulators clusters Activation state. Heat map is used to visualize these biological processes, red means activation, blue means inhibition. (E) The distribution of immune infiltration of 22 immune cells in 2 m6A regulators clusters (**p < 0.05, ****p < 0.001). (F) Differential cell prognosis analysis.