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This study introduces a machine learning model integrating 3D genome architecture to improve acute myeloid leukemia (AML) prediction using circular RNAs (circRNAs). The 3D genome-informed approach enhances biomarker discovery and predictive model robustness.

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Area of Science:

  • Genomics and Bioinformatics
  • Cancer Research
  • Molecular Biology

Background:

  • Acute myeloid leukemia (AML) is an aggressive cancer driven by genetic and epigenetic changes.
  • Circular RNAs (circRNAs) show promise as diagnostic biomarkers due to their stability.
  • Current circRNA models for AML prediction often overlook the role of 3D genome organization.

Purpose of the Study:

  • To develop a machine learning framework integrating 3D genome architecture for improved circRNA biomarker selection in AML.
  • To investigate the impact of spatial genome organization on circRNA formation, function, and predictive potential.

Main Methods:

  • Mapped 9,565 circRNAs onto a 3D chromatin model derived from Hi-C data.
  • Analyzed circRNA spatial clustering and pathway enrichment within the 3D genome.
  • Developed circRNA panels using expression, pathway, and spatial features, validated with machine learning algorithms.

Main Results:

  • Identified 18 pathways with significant 3D circRNA aggregation, allowing radial stratification.
  • A panel from the fifth radial layer (Panel-3DG-Radius5) demonstrated superior and consistent AML prediction performance (ROC-AUC > 0.99).
  • Integration of 3D genomic context reduced feature collinearity and improved biological interpretability.

Conclusions:

  • A 3D genome-informed paradigm can significantly enhance circRNA biomarker discovery for AML.
  • Spatial genome organization is a crucial factor for improving the precision and robustness of AML predictive models.
  • This approach offers a novel strategy for leveraging complex genomic data in cancer diagnostics.