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. 2021 Sep 28;12:698236. doi: 10.3389/fimmu.2021.698236

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Copyright © 2021 Zhou and Gao

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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The relationship between m⁶A-predictive score and immune infiltration. (A) Distribution of different immune scores (ESTIMATEScore, ImmuneScore and StromalScore) in low and high m⁶A-score groups. (B–D) The correlation analysis of PD-L1 expression levels and m⁶A-predictive score via Spearman correlation method in meta-GEO cohort, TCGA cohort and GSE72094 cohort. (E) The estimated fraction of different immune infiltrating cells calculated by CIBERSORT algorithm in different m⁶A-score groups (*P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001). (F) GSEA enrichment analysis (GO) of the top differentially expressed genes in the group with a high m⁶A-predictive score. (G) The estimated fraction of different immune infiltrating cells calculated by MCPcounter algorithm in different m⁶A-score groups. ns, not significant.