ORIGINAL RESEARCH article
Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1597633
This article is part of the Research TopicDiagnostic, Prognostic and Predictive Markers in LeukemiaView all 10 articles
Integrated bioinformatic analysis and machine learning developed a prognostic model based on mitochondrial function for acute myeloid leukemia
Provisionally accepted
- 1Guangzhou Medical University, Guangzhou, Guangdong Province, China
- 2Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong Province, China
- 3Dongguan University of Technology, Dongguan, Guangdong, China
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Background: The disease burden of acute myeloid leukemia (AML) continues to pose a significant public health challenge globally. Mitochondria play a critical role in tumor development and progression by influencing bioenergetics, biosynthesis, and signaling pathways. However, the prognostic significance and therapeutic implications of mitochondrial function in AML warrant further investigation. Methods: We integrated mitochondrial gene expression data with bulk RNA sequencing to identify key mitochondrial genes associated with AML. A total of fourteen machine learning algorithms were employed, yielding 148 unique combinations. The best-performing model was utilized to develop a MitoScore, which was then combined with clinical variables to establish a MitoScore-based nomogram. Additionally, single-cell sequencing data were analyzed to assess the impact of key mitochondrial genes on immune cells. Samples were classified into low-risk and high-risk groups based on MitoScore, allowing for a comparative analysis of clinical features, biological mechanisms, copy number variations, tumor burden, immune infiltration, immune function, and drug sensitivity between the two groups. Results: Specific expression patterns of mitochondrial genes were observed in T cell subsets and at various developmental stages of AML. Samples were classified into low-risk and high-risk groups based on MitoScore. The high-risk MitoScore group exhibited a worse prognosis, with enriched biological processes and molecular pathways associated with immune response, a higher frequency of gene mutations linked to poor outcomes, increased immune cell infiltration, enhanced immune function, upregulated immune checkpoint gene expression, and greater sensitivity to cyclophosphamide and venetoclax. Conclusions: This robust machine learning framework underscores the potential of MitoScore as a tool for stratified prognostic assessment and personalized treatment planning in AML patients.
Keywords: Mitochondrial-related molecular signature, Acute Myeloid Leukemia, single-cell RNAsequencing, Machine learning model, Immune function
Received: 21 Mar 2025; Accepted: 13 Oct 2025.
Copyright: © 2025 Lu, Chen, Ling, Yang and Chen. 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: Ziyuan Lu, lzo19880306@126.com
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