Fig. 6

The m6A-related feature gene selection and the diagnostic gene signature construction. a The random forest algorithm revealed that the error is small and stable after 400 nTree in the training set. b Eight feature genes were selected according to the cutoff value of 0.4, including KIAA1429, WTAP, YTHDF1, ALKBH5, YTHDF2, HNRNPC, METTL3, and YTHDC2. c The ROC curve for assessing the performance of this diagnostic gene signature in the training set. d The ROC curve for assessing the performance of this diagnostic gene signature in the test set