Machine learning prediction for AML based on 3D genome selected circRNA.

ABSTRACT: Acute myeloid leukemia (AML) is a clinically aggressive hematologic malignancy driven by complex genetic and epigenetic aberrations. Circular RNAs (circRNAs), characterized by covalently closed structures and exceptional stability, have emerged as promising diagnostic biomarkers. However, existing circRNA-based predictive models largely depend on differential expression, overlooking the potential impact of higher-order chromatin organization on circRNA formation and function. Here, we propose a machine learning framework that integrates three-dimensional (3D) genome architecture to refine circRNA selection for AML prediction. By mapping 9,565 circRNAs onto a 3D chromatin model reconstructed from Hi-C data, we analyzed their spatial clustering and biological pathway enrichment. Eighteen pat

SUBMITTER: Yuan Z 

PROVIDER: S-EPMC12868704 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature