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 8. Analysis of molecular characteristics of m6Ascore high and low groups. (A, B) Distribution of gene mutations in samples of m6Ascore high and low groups; (C, D) The distribution of copy number amplification and deletion regions in the sample set of m6Ascore high and low groups.