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 9. m6Ascore provided predictive outcomes for GBM patients receiving immunotherapies. (A, B) The difference between the IC50 values of Cisplatin and Gemcitabine in the samples of the high-risk group and the low-risk group; (C) The difference of TIDE score between samples of high-risk group and low-risk group; (D) Prediction of immunotherapy response results by using high and low groups of m6Ascore.