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

← Back

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 3. Comparative analysis between m6Acluster in the TCGA dataset. (A) The distribution of IDH1, EGFR, TP53 mutations in the 2 m6Aclusters; (B) The distribution of radiotherapy, gender, and age in m6Acluster; (C) The enrichment scores of different m6Acluster groups difference (**P<0.05, *** P<0.01, ****P<0.001).