ORIGINAL RESEARCH article
Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
This article is part of the Research TopicDiagnostic, Prognostic and Predictive Markers in LeukemiaView all 13 articles
Elucidating AML Ribosome Biogenesis Reshaping Prognostic Assessment and Immune Microenvironment Integrated Single-Cell and Bulk RNA Analysis
Provisionally accepted
- Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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Background: Acute Myeloid Leukemia (AML) is an aggressive hematologic malignancy with significant clinical challenges due to its heterogeneity and high relapse rate. Dysregulated ribosome biogenesis is a recognized driver of tumorigenesis and therapy resistance, but its comprehensive impact on AML prognosis and the immune microenvironment remains to be fully elucidated. Methods: We constructed an AML single-cell atlas and quantified ribosome biogenesis activity. A prognostic ribosome biogenesis signature (RBS) was developed through integrated machine learning and interpreted using the SHapley Additive exPlanations (SHAP) framework. We evaluated the RBS associations with prognosis, tumor immune microenvironment, immunotherapy response, and drug sensitivity. Key findings were validated by qPCR, Western blot, and molecular docking. Results: Single-cell analysis revealed significantly elevated ribosome biogenesis activity scores (RAS, a composite metric quantifying cellular ribosome synthesis capacity) in AML stem and progenitor cells. The RBS model, established using a Random Survival Forest algorithm, demonstrated robust prognostic power. SHAP interpretation identified EXOSC2 as the top predictor. Low-RBS patients displayed an immune-activated microenvironment and heightened immunotherapy response. Molecular docking indicated high-affinity binding of ouabain and digoxin to EXOSC2. Conclusion: Our study delineates the critical role of ribosome biogenesis in AML progression and establishes an interpretable RBS prognostic signature. This tool effectively assesses immunotherapy responsiveness and reveals novel targeting opportunities, providing valuable insights for clinical decision-making to improve AML outcomes.
Keywords: Acute Myeloid Leukemia, Ribosome biogenesis, single-cell RNA sequencing, machine learning, diagnostic biomarkers, immune microenvironment
Received: 10 Jun 2025; Accepted: 14 Nov 2025.
Copyright: © 2025 Chen, Chen, Feng, Chen and Zeng. 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: Ming Chen, chenming985017@wmu.edu.cn
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