• Jichun Ma, Hongchang Zhao, and Fang Ge.
• Affiliated People's Hospital of Jiangsu University, Zhenjiang, Jiangsu, China.
• Medicine (Baltimore). 2024 Jul 12; 103 (28): e38897e38897.

AbstractMyelodysplastic syndrome (MDS) frequently transforms into acute myeloid leukemia (AML). Predicting the risk of its transformation will help to make the treatment plan. Levels of expression of N6-methyladenosine (m6A) regulators is difference in patients with AML, MDS, and MDS transformed into AML. Seven machine learning algorithms were established based on all of 26 m6A or main differentially expressed m6A regulator genes, and attempted to establish a risk assessment method to distinguish AML from MDS and predict the transformation of MDS into AML. In collective of m6A regulators sets, support vector machine (SVM) and neural network (NNK) model best distinguished AML or MDS from control, with area under the ROC curve (AUROC) 0.966 and 0.785 respectively. The SVM model best distinguished MDS from AML, with AUROC 0.943, sensitivity 0.862, specificity 0.864, and accuracy 0.864. In differentially expressed gene sets, SVM and logistic regression (LR) model best distinguished AML or MDS from control, with AUROC 0.945 and 0.801 respectively. The random forest (RF) model best distinguished between MDS and AML, with AUROC 0.928, sensitivity 0.725, specificity 0.898, and accuracy 0.818. For predictive capacity of MDS transformed into AML, SVM model showed the best predicted in collective m6A regulators sets, with AUROC 0.781 and accuracy 0.740. The LR model showed the best predicted in differential expression m6A regulators sets, with AUROC 0.820 and accuracy 0.760. All results suggested that machine learning model established by m6A regulators can be used to distinguished AML or MDS from control, distinguished AML from MDS and predicted the transformation of MDS into AML.Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.

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