Evidence map›Paper›PMID 41326769›Full record

ArticleNPJ digital medicine2025

Machine learning-driven glycolytic subtyping and exosome-based PKM splicing modulation overcome drug resistance in hyper-glycolytic myeloid leukemia.

Fangmin Zhong, Song Chen, Fangyi Yao, Jing Liu, Jing Zhang, Junyao Jiang, Qin Bai, Zihao Wang, Liqing Zhang, Li Liu and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Fangmin Zhong *Jiangxi Province Key Laboratory of Immunology and Inflammation, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Song Chen *Jiangxi Province Key Laboratory of Immunology and Inflammation, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Fangyi YaoJiangxi Province Key Laboratory of Immunology and Inflammation, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Jing LiuJiangxi Province Key Laboratory of Immunology and Inflammation, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Jing ZhangJiangxi Province Key Laboratory of Immunology and Inflammation, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Junyao JiangJiangxi Province Key Laboratory of Immunology and Inflammation, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Qin BaiJiangxi Province Key Laboratory of Immunology and Inflammation, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Zihao WangJiangxi Province Key Laboratory of Immunology and Inflammation, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Liqing ZhangSchool of Public Health, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Li LiuSchool of Public Health, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Xiaoxuan JiangSchool of Public Health, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Siying HuoSchool of Public Health, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Xiaozhong WangJiangxi Province Key Laboratory of Immunology and Inflammation, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China. wangxiaozhong@ncu.edu.cn.
Bo HuangJiangxi Province Key Laboratory of Immunology and Inflammation, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China. 764019522@qq.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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). An optimized 9-gene prognostic signature was developed using CoxBoost and StepCox algorithms, demonstrating accurate survival prediction across cohorts. Crucially, aberrant PKM2 overexpression was linked to imatinib (IM) resistance. A vivo-morpholino antisense oligomer (vMO) targeting the PKM exon 9-10 splice junction effectively converted PKM splicing from PKM2 to PKM1, inhibiting leukemia growth and reversing IM resistance in vitro and in vivo. To mitigate vMO toxicity, IL3-Lamp2b-engineered exosomes were developed, demonstrating efficient vMO loading, targeted delivery to leukemia cells, potent PKM splicing correction, significant IM resistance reversal, and minimal stromal cell toxicity. This work defines glycolysis-based AML subtypes with therapeutic implications and establishes engineered exosome-delivered vMO as a promising strategy to overcome drug resistance in hyper-glycolytic myeloid leukemia.

Identifiers

PMID41326769
PMCPMC12780028

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.