ORIGINAL RESEARCH article
Front. Oncol.
Sec. Cancer Molecular Targets and Therapeutics
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1649594
This article is part of the Research TopicInnovative Strategies for the Discovery of New Therapeutic Targets in Cancer TreatmentView all 7 articles
Integrated machine learning-based establishment of a prognostic model in multicenter cohorts for acute myeloid leukemia
Provisionally accepted
- 1Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
- 2Department of oral and maxillofacial surgery ，Tianjin Union Medical Center, The First Affiliated Hospital of Nankai University, Tianjin, China
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Background: Acute myeloid leukemia (AML) is a highly heterogeneous malignancy, with leukemic cell diversity contributing to disease progression and treatment resistance. This study aimed to evaluate the functional and prognostic significance of leukemic cell-related genes. Methods: We analyzed single-cell RNA sequencing data to identify malignant marker genes in AML. Consensus clustering was used to assess associations with prognosis and immune responses. A prognostic model, the malignant leukemia marker gene prognostic signature (MLAPS), was developed using 101 models across 10 machine learning algorithms and validated in five independent cohorts. Functional as-says were conducted to explore the role of CD69. Results: We identified a set of malignant marker genes significantly correlated with prognosis and immune classification. The MLAPS showed strong predictive performance, surpassing most clinical features and previously published signatures. Experimental validation confirmed that CD69 promotes malignant progression in AML. Conclusion: This study highlights the clinical value of leukemic cell-specific genes and presents MLAPS as a robust prognostic tool. CD69 may serve as a potential therapeutic target in AML.
Keywords: Acute Myeloid Leukemia, machine learning, prognosis, RNA-Seq, Tumor Microenvironment
Received: 18 Jun 2025; Accepted: 24 Sep 2025.
Copyright: © 2025 Lin, Yu, Xu and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mingcheng Liu, liumingcheng@ihcams.ac.cn
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