基于机器学习的糖酵解亚型分类和外泌体介导的PKM剪接调控克服高糖酵解髓系白血病中的药物耐药性

核心信息

• 论文英文标题: Machine learning-driven glycolytic subtyping and exosome-based PKM splicing modulation overcome drug resistance in hyper-glycolytic myeloid leukemia
• 期刊: NPJ Digit Med
• 发表年份: 2025
• DOI: 10.1038/s41746-025-02185-x
• PMID/PMCID: 41326769; PMC12780028

研究概览

This study comprehensively investigates the role of glycolysis in acute myeloid leukemia (AML) pathogenesis. Elevated glycolysis correlated significantly with poor prognosis.

核心发现

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 角色

ExoLoad装载吗啉代反义寡聚体vMO

关联平台: Echosome®, Research Reagents & Tools

关联服务与能力: Small RNA / Cargo Loading, Research Reagent / Product Supply

关联产品或试剂: ExoLoad® Nucleic Acid Loading Kit

参考文献

原始论文: 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.