Machine learning approaches reveal methylation signatures associated with pediatric acute myeloid leukemia recurrence.

ABSTRACT: Acute myeloid leukemia (AML) is a severe hematological malignancy characterized by high recurrence rates, especially in pediatric patients, highlighting the need for reliable prognostic markers. This study proposes methylation signatures associated with AML recurrence using computational methods. DNA methylation data from 696 newly diagnosed and 194 relapsed pediatric AML patients were analyzed. Feature selection algorithms, including Boruta, least absolute shrinkage and selection operator, light gradient boosting machine, and Monte Carlo feature selection, were employed to screen and rank methylation sites strongly correlated with AML recurrence. Incremental Feature Selection was performed to evaluate these results, and optimal subsets were identified using Decision Tree and Random Forest

SUBMITTER: Dong Y 

PROVIDER: S-EPMC12056120 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature