AutoML identification of microRNA biomarkers in high-risk pediatric acute lymphoblastic leukemia.

ABSTRACT: Despite significant advancements in overall survival rates for childhood acute lymphoblastic leukemia (ALL), relapse continues to pose a major challenge. MicroRNAs have proven valuable for improving diagnosis, treatment, and survival outcomes, establishing themselves as key biomarkers. Using RNA-seq data from 123 ALL patients and employing predictive modeling via automated machine learning (AutoML) alongside causal-inspired biomarker discovery, we identified highly predictive microRNA signatures linked to high-risk strata and clinical features in unfavorable cases. We further identified predictive signatures for each genetic subtype of childhood ALL, highlighting shared miRNAs throughout the study. A thorough literature review of the relationships between miRNA differential expression and

SUBMITTER: Kyriakidis I 

PROVIDER: S-EPMC12418865 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature