骨髓增生异常综合征 (MDS) 经常转化为急性髓系白血病 (AML)。预测其转变的风险将有助于制定治疗计划。 N6-甲基腺苷（m6A）调节因子的表达水平在AML、MDS和MDS转化为AML的患者中存在差异。基于全部26个m6A或主要差异表达的m6A调节基因建立了7种机器学习算法，试图建立区分AML和MDS的风险评估方法，并预测MDS向AML的转化。在 m6A 调节器集合中，支持向量机 (SVM) 和神经网络 (NNK) 模型能够最好地区分 AML 或 MDS 与对照，ROC 曲线下面积 (AUROC) 分别为 0.966 和 0.785。 SVM 模型能够最好地区分 MDS 和 AML，其 AUROC 0.943、敏感性 0.862、特异性 0.864 和准确性 0.864。在差异表达基因集中，SVM 和逻辑回归 (LR) 模型能够最好地将 AML 或 MDS 与对照区分开来，AUROC 分别为 0.945 和 0.801。随机森林 (RF) 模型能够最好地区分 MDS 和 AML，其 AUROC 0.928、敏感性 0.725、特异性 0.898 和准确性 0.818。对于 MDS 转化为 AML 的预测能力，SVM 模型在 m6A 调节因子集合中表现出最佳预测，AUROC 0.781，准确度 0.740。 LR 模型显示了差异表达 m6A 调节器组中的最佳预测，AUROC 0.820，准确度 0.760。所有结果表明，m6A 监管机构建立的机器学习模型可用于区分 AML 或 MDS 与对照，区分 AML 与 MDS，并预测 MDS 向 AML 的转变。版权所有 © 2024 作者。由 Wolters Kluwer Health, Inc. 出版

Myelodysplastic 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.