Machine learning-driven glycolytic subtyping and exosome-based PKM splicing modulation overcome drug resistance in hyper-glycolytic myeloid leukemia
Quick Facts
- Publication title: Machine learning-driven glycolytic subtyping and exosome-based PKM splicing modulation overcome drug resistance in hyper-glycolytic myeloid leukemia
- Journal: NPJ Digit Med
- Year: 2025
- DOI: 10.1038/s41746-025-02185-x
- PMID/PMCID: 41326769; PMC12780028
Research overview
This study comprehensively investigates the role of glycolysis in acute myeloid leukemia (AML) pathogenesis. Elevated glycolysis correlated significantly with poor prognosis.
Key findings
This study comprehensively investigates the role of glycolysis in acute myeloid leukemia (AML) pathogenesis.
Elevated glycolysis correlated significantly with poor prognosis.
Bioinformatics identified HIF1A and MIF as key regulators and revealed two robust molecular subtypes: a high-glycolysis subtype (C1) associated with increased malignant cell proportion, activated oncogenic pathways, genomic instability, and inferior survival, and a low-glycolysis subtype (C2).
These subtypes exhibited distinct drug sensitivities (C1 sensitive to panobinostat, MK-2206, 17-AAG; C2 sensitive to venetoclax) and predicted immunotherapy responses (C1 potentially benefiting more from anti-PD-1).
Echo Biotech Role
Echo Biotech contributed cargo loading; the study also used or cited ExoLoad®.
Related platforms: Echosome®, Research Reagents & Tools
Related services and capabilities: Small RNA / Cargo Loading, Research Reagent / Product Supply
Related products or reagents: ExoLoad® Nucleic Acid Loading Kit
References
Original publication: Machine learning-driven glycolytic subtyping and exosome-based PKM splicing modulation overcome drug resistance in hyper-glycolytic myeloid leukemia NPJ digital medicine. 2025. DOI: 10.1038/s41746-025-02185-x. PMID/PMCID: 41326769; PMC12780028.