Fig. 4

Machine learning from the combined genomic and expression features predicts patient prognosis. a Bubble plot showing the trends of features in terms of TMZ-resistant and TMZ-sensitive. The bubble size indicates P-value, the color and location of the bubble indicate the log2 of TMZ-resistant ratio/TMZ-Sensitive ratio value. If the log2 TMZ-Resistant ratio/TMZ-Sensitive ratio value is positive, the bubble is colored in red, and if negative, it is colored in blue. Copy number gain and loss were not counted in this plot. del: deletion, amp: amplification, exp: expression, subtype: GBM subtype; CL, classical; PN, proneural; MES, mesenchymal; M, methylated; UM, unmethylated. P values on gene expression and MGMT fusion bubbles are by t-test, the rest are by Fisher’s exact test. b ROC curve in the training set (n = 69). All features include 25 features shown in a. P < 0.01, using features that are P < 0.01 in a (gene expression of ANXA3, PAPPA, EGR4; AUC: 0,81); P < 0.001, using features that are P < 0.001 in a (ANXA3 expression; AUC: 0.77); MGMT, only using MGMT promoter status as prediction feature. c Sankey diagram showing confusion matrix of resistant and sensitive samples in the training dataset. Sen, TMZ-sensitive; Res, TMZ-resistant. d, e Survival curves of TCGA IDH-wt, TMZ-treated primary GBM samples which the TMZ response has been predicted by machine learning. P-values were calculated by logrank test. f, g Survival curves of mesenchymal TCGA samples separated by predicted TMZ response. P-values were computed by log-rank test