ORIGINAL RESEARCH article
Front. Immunol.
Sec. Systems Immunology
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1565954
This article is part of the Research TopicAdvances in Cancer Immunology and Immunotherapy for Acute Myeloid LeukemiaView all articles
Construction a novel inflammatory-related prognostic signature of acute myelocytic leukemia based on conjoint analysis of signalsingle cell and bulk RNA sequencing
Provisionally accepted
Yongfen Huang1Ping Yi2,3Yixuan Wang4Lingling Wang1Yongqin Cao1Jingbo Lu1Kun Fang2,3
Yuexin Cheng1
Yu-Qing Miao1*- 1The First People’s Hospital of Yancheng, Yancheng, China
- 2Department of Scientific Research Project, Wuhan Kindstar Medical Laboratory Co., Ltd.,, Wuhan, Hebei Province, China
- 3Kindstar Global Precision Medicine Institute, Wuhan, China
- 4Yancheng Clinical College, Xuzhou Medical University, Yancheng, China
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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. scRNA seq classified cells into 9 clusters, including B cells, 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 1594 marker genes. Furthermore, we identified AML-associated genes (2067 genes) and DEGs (1010 genes) between AML patients and controls using GSE114868 dataset. After performing intersection, univariate Cox, and LASSO analysis, we obtained a prognostic model based on the expression levels of 5 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-risk and low-risk groups. In conclusion, we constructed an inflammation-related risk model using internal scRNA data and external bulk RNA data, that can accurately distinguish survival outcomes in AML patients.
Keywords: Acute Myeloid Leukemia, ScRNA-seq, Bulk RNA-seq, Prognostic signature, Inflammation
Received: 13 Feb 2025; Accepted: 05 May 2025.
Copyright: © 2025 Huang, Yi, Wang, Wang, Cao, Lu, Fang, Cheng and Miao. 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: Yu-Qing Miao, The First People’s Hospital of Yancheng, Yancheng, China
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