ArticleFrontiers in immunology2025
Construction of a novel inflammatory-related prognostic signature of acute myelocytic leukemia based on conjoint analysis of single-cell and bulk RNA sequencing.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Defining the role of natural killer cells in acute myeloid leukemia through the lens of single-cell omics.Frontiers in immunology · 2026Review
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9 authors.
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Abstract
Introduction: The prognostic management of acute myeloid leukemia (AML) remains a challenge for clinicians. This study aims to construct a novel risk model for AML patient through comprehensive analysis of scRNA and bulk RNA data to optimize the precise treatment strategies for patients and improve prognosis. Methods and Results: scRNA-seq classified cells into nine clusters, including Bcells, erythrocyte, granulocyte-macrophage progenitor (GMP), hematopoietic stem cell progenitors (HSC/Prog), monocyte/macrophagocyte (Mono/Macro), myelocyte, neutrophils, plasma, and T/NK cells. Functional analysis demonstrated the important role of inflammation immune response in the pathogenesis of AML, and the leukocyte transendothelial migration and adhesion in the process of inflammation should be noticed. ssGSEA method identified four core cells including GMP, HSC/Prog, Mono/Macro, and myelocyte for subsequent analysis, which contains 1,594 marker genes. Furthermore, we identified AML-associated genes (2,067genes) and DEGs (1,010genes) between AML patients and controls usingGSE114868dataset. After performing intersection, univariate Cox, and LASSO analysis, we obtained a prognostic model based on the expression levels of five signature genes, namely, CALR, KDM1A, SUCNR1, TMEM220, and ADM. The prognostic model was then validated by two external datasets. Patients with high-risk scores are predisposed to experience poor overall survival. Further GSEA analysis of risk-model-related genes revealed the significant differences in inflammatory response between high-and low-risk groups. Conclusion: In conclusion, we constructed an inflammation related risk model using internal scRNA data and external bulk RNA data, which can accurately distinguish survival outcomes in AML patients.
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