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

Chuanyu Li; Wangrui Liu; Chengming Liu; Qisheng Luo; Kunxiang Luo; Cuicui Wei; Xueyu Li; Jiancheng Qin; Chuanhua Zheng; Chuanliu Lan; Shiyin Wei; Rong Tan; Jiaxing Chen; Yuanbiao Chen; Huadong Huang; Gaolian Zhang; Haineng Huang; Xiangyu Wang

doi:doi:10.18632/aging.204495

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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

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).