Cellular heterogeneity and cytokine signatures in acute myeloid leukemia: A novel prognostic model
Acute Myeloid Leukemia (AML) is a complex hematological malignancy distinguished by its heterogeneity in genetic aberrations, cellular composition, and clinical outcomes. This diversity complicates the development of effective, universally applicable therapeutic strategies and highlights the necessi...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Translational Oncology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1936523324003206 |
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| author | Jinxia Cao Bin Hu Tianqi Li Dan Fang ling Jiang Jun Wang |
| author_facet | Jinxia Cao Bin Hu Tianqi Li Dan Fang ling Jiang Jun Wang |
| author_sort | Jinxia Cao |
| collection | DOAJ |
| description | Acute Myeloid Leukemia (AML) is a complex hematological malignancy distinguished by its heterogeneity in genetic aberrations, cellular composition, and clinical outcomes. This diversity complicates the development of effective, universally applicable therapeutic strategies and highlights the necessity for personalized approaches to treatment. In our study, we utilized high-resolution single-cell RNA sequencing from publicly available datasets to dissect the complex cellular landscape of AML. This approach uncovered a diverse array of cellular subpopulations within the bone marrow samples of AML patients. Through meticulous analysis, we identified 156 differentially expressed cytokine-related genes that underscore the nuanced interplay between AML cells and their microenvironment. Leveraging this comprehensive dataset, we constructed a prognostic risk score model based on seven pivotal cytokine-related genes: CCL23, IL2RA, IL3RA, IL6R, INHBA, TNFSF15, and TNFSF18. The mRNA levels of 7 genes in the risk score model have significant different. This model was rigorously validated across several independent AML patient cohorts, showcasing its robust prognostic capability to stratify patients into distinct risk categories. Patients classified under the high-risk category exhibited significantly poorer survival outcomes compared to their low-risk counterparts, underscoring the model's clinical relevance. Additionally, our in-depth investigation into the immune landscape revealed marked differences in immune cell infiltration and cytokine signaling between the identified risk groups, shedding light on potential immune-mediated mechanisms driving disease progression and treatment resistance. This comprehensive analysis not only advances our understanding of the cellular and molecular underpinnings of AML but also introduces a novel, clinically applicable risk score model. This tool holds significant promise for enhancing the precision of prognostic assessments in AML, thereby paving the way for more tailored and effective therapeutic interventions. Our findings represent a pivotal step toward the realization of personalized medicine in the management of AML, offering new avenues for research and treatment optimization in this challenging disease landscape. |
| format | Article |
| id | doaj-art-22a326173069410e8db0f349a3c0cbe1 |
| institution | Kabale University |
| issn | 1936-5233 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Translational Oncology |
| spelling | doaj-art-22a326173069410e8db0f349a3c0cbe12025-01-22T05:41:22ZengElsevierTranslational Oncology1936-52332025-02-0152102194Cellular heterogeneity and cytokine signatures in acute myeloid leukemia: A novel prognostic modelJinxia Cao0Bin Hu1Tianqi Li2Dan Fang3ling Jiang4Jun Wang5Department of Hematology, Changde Hospital, Xiangya School of Medicine, Central South University (The first people’s hospital of Changde city), Wuling District, Changde, Hunan Province, ChinaDepartment of Hematology, Changde Hospital, Xiangya School of Medicine, Central South University (The first people’s hospital of Changde city), Wuling District, Changde, Hunan Province, ChinaDepartment of Hematology, Changde Hospital, Xiangya School of Medicine, Central South University (The first people’s hospital of Changde city), Wuling District, Changde, Hunan Province, ChinaDepartment of Hematology, Changde Hospital, Xiangya School of Medicine, Central South University (The first people’s hospital of Changde city), Wuling District, Changde, Hunan Province, ChinaDepartment of Hematology, Changde Hospital, Xiangya School of Medicine, Central South University (The first people’s hospital of Changde city), Wuling District, Changde, Hunan Province, ChinaCorresponding author.; Department of Hematology, Changde Hospital, Xiangya School of Medicine, Central South University (The first people’s hospital of Changde city), Wuling District, Changde, Hunan Province, ChinaAcute Myeloid Leukemia (AML) is a complex hematological malignancy distinguished by its heterogeneity in genetic aberrations, cellular composition, and clinical outcomes. This diversity complicates the development of effective, universally applicable therapeutic strategies and highlights the necessity for personalized approaches to treatment. In our study, we utilized high-resolution single-cell RNA sequencing from publicly available datasets to dissect the complex cellular landscape of AML. This approach uncovered a diverse array of cellular subpopulations within the bone marrow samples of AML patients. Through meticulous analysis, we identified 156 differentially expressed cytokine-related genes that underscore the nuanced interplay between AML cells and their microenvironment. Leveraging this comprehensive dataset, we constructed a prognostic risk score model based on seven pivotal cytokine-related genes: CCL23, IL2RA, IL3RA, IL6R, INHBA, TNFSF15, and TNFSF18. The mRNA levels of 7 genes in the risk score model have significant different. This model was rigorously validated across several independent AML patient cohorts, showcasing its robust prognostic capability to stratify patients into distinct risk categories. Patients classified under the high-risk category exhibited significantly poorer survival outcomes compared to their low-risk counterparts, underscoring the model's clinical relevance. Additionally, our in-depth investigation into the immune landscape revealed marked differences in immune cell infiltration and cytokine signaling between the identified risk groups, shedding light on potential immune-mediated mechanisms driving disease progression and treatment resistance. This comprehensive analysis not only advances our understanding of the cellular and molecular underpinnings of AML but also introduces a novel, clinically applicable risk score model. This tool holds significant promise for enhancing the precision of prognostic assessments in AML, thereby paving the way for more tailored and effective therapeutic interventions. Our findings represent a pivotal step toward the realization of personalized medicine in the management of AML, offering new avenues for research and treatment optimization in this challenging disease landscape.http://www.sciencedirect.com/science/article/pii/S1936523324003206Acute myeloid leukemiaSingle-cell sequencingCytokine signalingRisk score modelCellular heterogeneity |
| spellingShingle | Jinxia Cao Bin Hu Tianqi Li Dan Fang ling Jiang Jun Wang Cellular heterogeneity and cytokine signatures in acute myeloid leukemia: A novel prognostic model Translational Oncology Acute myeloid leukemia Single-cell sequencing Cytokine signaling Risk score model Cellular heterogeneity |
| title | Cellular heterogeneity and cytokine signatures in acute myeloid leukemia: A novel prognostic model |
| title_full | Cellular heterogeneity and cytokine signatures in acute myeloid leukemia: A novel prognostic model |
| title_fullStr | Cellular heterogeneity and cytokine signatures in acute myeloid leukemia: A novel prognostic model |
| title_full_unstemmed | Cellular heterogeneity and cytokine signatures in acute myeloid leukemia: A novel prognostic model |
| title_short | Cellular heterogeneity and cytokine signatures in acute myeloid leukemia: A novel prognostic model |
| title_sort | cellular heterogeneity and cytokine signatures in acute myeloid leukemia a novel prognostic model |
| topic | Acute myeloid leukemia Single-cell sequencing Cytokine signaling Risk score model Cellular heterogeneity |
| url | http://www.sciencedirect.com/science/article/pii/S1936523324003206 |
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