AUTHOR=Yan Lin , Yu Huasong , Xu Xiaoyi , Liu Mingcheng 

TITLE=Integrated machine learning-based establishment of a prognostic model in multicenter cohorts for acute myeloid leukemia

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1649594

DOI=10.3389/fonc.2025.1649594

ISSN=2234-943X

ABSTRACT=BackgroundAcute 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.MethodsWe 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 assays were conducted to explore the role of CD69.ResultsWe 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.ConclusionThis 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.