Figure 1 — Integrating machine learning and bioinformatics analysis to m6A regulator-mediated methylation modification models for predicting glioblastoma patients’ prognosis and immunotherapy response

Research Paper|Volume 15, Issue 10|pp. 4051–4070

Integrating machine learning and bioinformatics analysis to m6A regulator-mediated methylation modification models for predicting glioblastoma patients’ prognosis and immunotherapy response

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Figure 1.Genetic variation of m6A regulator. (A) m6A expression of tumor and normal samples in TCGA; (B) Distribution of m6A gene mutations and different mutation types; (C) CNV incidence of m6A gene, blue indicates deletion and orange indicates amplification; (D) Position of m6A gene on chromosome; (E) PCA results of m6A gene in TCGA samples).