Construction of a novel inflammatory-related prognostic signature of acute myelocytic leukemia based on conjoint analysis of single-cell and bulk RNA sequencing
IntroductionThe 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 pr...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1565954/full |
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| author | Yongfen Huang Ping Yi Ping Yi Yixuan Wang Lingling Wang Yongqin Cao Jingbo Lu Kun Fang Kun Fang Yuexin Cheng Yuqing Miao Yuqing Miao |
| author_facet | Yongfen Huang Ping Yi Ping Yi Yixuan Wang Lingling Wang Yongqin Cao Jingbo Lu Kun Fang Kun Fang Yuexin Cheng Yuqing Miao Yuqing Miao |
| author_sort | Yongfen Huang |
| collection | DOAJ |
| description | IntroductionThe 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 ResultsscRNA-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.ConclusionIn 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. |
| format | Article |
| id | doaj-art-d77b3999b96c4c7e9cc5da9a85f9d933 |
| institution | OA Journals |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Immunology |
| spelling | doaj-art-d77b3999b96c4c7e9cc5da9a85f9d9332025-08-20T02:08:35ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-06-011610.3389/fimmu.2025.15659541565954Construction of a novel inflammatory-related prognostic signature of acute myelocytic leukemia based on conjoint analysis of single-cell and bulk RNA sequencingYongfen Huang0Ping Yi1Ping Yi2Yixuan Wang3Lingling Wang4Yongqin Cao5Jingbo Lu6Kun Fang7Kun Fang8Yuexin Cheng9Yuqing Miao10Yuqing Miao11Department of Hematology, Yancheng No.1 People’s Hospital, Yancheng, ChinaDepartment of Scientific Research Project, Wuhan Kindstar Medical Laboratory Co., Ltd., Wuhan, ChinaKindstar Global Precision Medicine Institute, Wuhan, ChinaYancheng Clinical College, Xuzhou Medical University, Yancheng, ChinaDepartment of Hematology, Yancheng No.1 People’s Hospital, Yancheng, ChinaDepartment of Hematology, Yancheng No.1 People’s Hospital, Yancheng, ChinaDepartment of Hematology, Yancheng No.1 People’s Hospital, Yancheng, ChinaDepartment of Scientific Research Project, Wuhan Kindstar Medical Laboratory Co., Ltd., Wuhan, ChinaKindstar Global Precision Medicine Institute, Wuhan, ChinaDepartment of Hematology, Yancheng No.1 People’s Hospital, Yancheng, ChinaDepartment of Hematology, Yancheng No.1 People’s Hospital, Yancheng, ChinaYancheng Clinical College, Xuzhou Medical University, Yancheng, ChinaIntroductionThe 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 ResultsscRNA-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.ConclusionIn 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.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1565954/fullacute myeloid leukemiaScRNA-seqbulk RNA-seqprognostic signatureinflammation |
| spellingShingle | Yongfen Huang Ping Yi Ping Yi Yixuan Wang Lingling Wang Yongqin Cao Jingbo Lu Kun Fang Kun Fang Yuexin Cheng Yuqing Miao Yuqing Miao Construction of a novel inflammatory-related prognostic signature of acute myelocytic leukemia based on conjoint analysis of single-cell and bulk RNA sequencing Frontiers in Immunology acute myeloid leukemia ScRNA-seq bulk RNA-seq prognostic signature inflammation |
| title | Construction of a novel inflammatory-related prognostic signature of acute myelocytic leukemia based on conjoint analysis of single-cell and bulk RNA sequencing |
| title_full | Construction of a novel inflammatory-related prognostic signature of acute myelocytic leukemia based on conjoint analysis of single-cell and bulk RNA sequencing |
| title_fullStr | Construction of a novel inflammatory-related prognostic signature of acute myelocytic leukemia based on conjoint analysis of single-cell and bulk RNA sequencing |
| title_full_unstemmed | Construction of a novel inflammatory-related prognostic signature of acute myelocytic leukemia based on conjoint analysis of single-cell and bulk RNA sequencing |
| title_short | Construction of a novel inflammatory-related prognostic signature of acute myelocytic leukemia based on conjoint analysis of single-cell and bulk RNA sequencing |
| title_sort | construction of a novel inflammatory related prognostic signature of acute myelocytic leukemia based on conjoint analysis of single cell and bulk rna sequencing |
| topic | acute myeloid leukemia ScRNA-seq bulk RNA-seq prognostic signature inflammation |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1565954/full |
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