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Li Q, Pan H, Gao Z, Li W, Zhang L, Zhao J, Fang L, Chu Y, Yuan W, Shi J. High-expression of the innate-immune related gene UNC93B1 predicts inferior outcomes in acute myeloid leukemia. Front Genet 2023; 14:1063227. [PMID: 36741319 PMCID: PMC9891309 DOI: 10.3389/fgene.2023.1063227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 01/06/2023] [Indexed: 01/19/2023] Open
Abstract
Acute myeloid leukemia (AML) is a heterogeneous hematological malignancy with dismal prognosis. Identification of better biomarkers remained a priority to improve established stratification and guide therapeutic decisions. Therefore, we extracted the RNA sequence data and clinical characteristics of AML from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression database (GTEx) to identify the key factors for prognosis. We found UNC93B1 was highly expressed in AML patients and significantly linked to poor clinical features (p < 0.05). We further validated the high expression of UNC93B1 in another independent AML cohort from GEO datasets (p < 0.001) and performed quantitative PCR of patient samples to confirm the overexpression of UNC93B1 in AML (p < 0.005). Moreover, we discovered high level of UNC93B1 was an independent prognostic factor for poorer outcome both in univariate analysis and multivariate regression (p < 0.001). Then we built a nomogram model based on UNC93B1 expression, age, FAB subtype and cytogenetic risk, the concordance index of which for predicting overall survival was 0.729 (p < 0.001). Time-dependent ROC analysis for predicting survival outcome at different time points by UNC93B1 showed the cumulative 2-year survival rate was 43.7%, and 5-year survival rate was 21.9%. The differentially expressed genes (DEGs) between two groups divided by UNC93B1 expression level were enriched in innate immune signaling and metabolic process pathway. Protein-protein interaction (PPI) network indicated four hub genes (S100A9, CCR1, MRC1 and CD1C) interacted with UNC93B1, three of which were also significantly linked to inferior outcome. Furthermore, we discovered high UNC93B1 tended to be infiltrated by innate immune cells, including Macrophages, Dendritic cells, Neutrophils, Eosinophils, and NK CD56dim cells. We also found UNC93B1 had a significantly positive correlation with CD14, CD68 and almost all Toll-like receptors. Finally, we revealed negatively correlated expression of UNC93B1 and BCL2 in AML and conjectured that high-UNC93B1 monocytic AML is more resistant to venetoclax. And we found high MCL-1 expression compensated for BCL-2 loss, thus, we proposed MCL-1 inhibitor might overcome the resistance of venetoclax in AML. Altogether, our findings demonstrated the utility of UNC93B1 as a powerful poor prognostic predictor and alternative therapeutic target.
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Affiliation(s)
- Qiaoli Li
- Regenerative Medicine Clinic, State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Hong Pan
- Regenerative Medicine Clinic, State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Zhen Gao
- Regenerative Medicine Clinic, State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Weiwang Li
- Regenerative Medicine Clinic, State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Lele Zhang
- Regenerative Medicine Clinic, State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Jingyu Zhao
- Regenerative Medicine Clinic, State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Liwei Fang
- Regenerative Medicine Clinic, State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Yajing Chu
- Center for Stem Cell Medicine and Department of Stem Cell & Regenerative Medicine, State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Weiping Yuan
- Center for Stem Cell Medicine and Department of Stem Cell & Regenerative Medicine, State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Jun Shi
- Regenerative Medicine Clinic, State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China,*Correspondence: Jun Shi,
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Lu J, Zheng G, Dong A, Chang X, Cao X, Liu M, Shi X, Wang C, Yang Y, Jia X. Prognostic characteristics of immune subtypes associated with acute myeloid leukemia and their identification in cell subsets based on single-cell sequencing analysis. Front Cell Dev Biol 2022; 10:990034. [PMID: 36211454 PMCID: PMC9540204 DOI: 10.3389/fcell.2022.990034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 09/05/2022] [Indexed: 11/19/2022] Open
Abstract
Immune genes play an important role in the development and progression of acute myeloid leukemia (AML). However, the role of immune genes in the prognosis and microenvironment of AML remains unclear. In this study, we analyzed 151 AML patients in the TCGA database for relevant immune cell infiltration. AML patients were divided into high and low immune cell infiltration clusters based on ssGSEA results. Immune-related pathways, AML pathways and glucose metabolism pathways were enriched in the high immune cell infiltration cluster. Then we screened the differential immune genes between the two immune cell infiltration clusters. Nine prognostic immune genes were finally identified in the train set by LASSO-Cox regression. We constructed a model in the train set based on the nine prognostic immune genes and validated the predictive capability in the test set. The areas under the ROC curve of the train set and the test set for ROC at 1, 3, 5 years were 0.807, 0.813, 0.815, and 0.731, 0.745, 0.830, respectively. The areas under ROC curve of external validation set in 1, 3, and 5 years were 0.564, 0.619, and 0.614, respectively. People with high risk scores accompanied by high TMB had been detected with the worst prognosis. Single-cell sequencing analysis revealed the expression of prognostic genes in AML cell subsets and pseudo-time analysis described the differentiation trajectory of cell subsets. In conclusion, our results reveal the characteristics of immune microenvironment and cell subsets of AML, while it still needs to be confirmed in larger samples studies. The prognosis model constructed with nine key immune genes can provide a new method to assess the prognosis of AML patients.
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Affiliation(s)
- Jie Lu
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Guowei Zheng
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Ani Dong
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Xinyu Chang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Xiting Cao
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Mengying Liu
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Xuezhong Shi
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Chunmei Wang
- Children’s Hospital, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yongli Yang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
| | - Xiaocan Jia
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China
- *Correspondence: Xiaocan Jia,
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Tools for optimizing risk assessment in hematopoietic cell transplant - What can we get away with? Hum Immunol 2022; 83:704-711. [PMID: 35120770 DOI: 10.1016/j.humimm.2022.01.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 12/20/2021] [Accepted: 01/17/2022] [Indexed: 12/13/2022]
Abstract
Unrelated allogeneic hematopoietic cell transplant (HCT) is a critical modality to treat hematologic malignancies. The current objective of donor selection is to match donor and recipient at the HLA (human leukocyte antigen) peptide-binding region which should lower the risk of graft-versus-host disease. However, depending on the patient's ethnicity/race, finding a matched donor is challenging, especially for HLA-DPB1 which is due to the weak linkage disequilibrium between HLA-DPB1 and the other HLA class II loci. Recent evidence, on the molecular level, has shown that certain HLA mismatches carry lower clinical risk. More specifically, there is an increasing understanding of polymorphisms of the innate and adaptive immune systems and their impact on transplant outcomes, allowing us to expand our "toolkit" for optimization of donor selection in HCT. Therefore, in this review we discuss matching strategies based on comparing donor and recipient polymorphisms that may influence innate and adaptive immune response genes in allorecognition and the role of single nucleotide polymorphisms in non-HLA genes that have the potential for providing additional tools to refine risk stratification.
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The Impact of NLRP3 Activation on Hematopoietic Stem Cell Transplantation. Int J Mol Sci 2021; 22:ijms222111845. [PMID: 34769275 PMCID: PMC8584591 DOI: 10.3390/ijms222111845] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 10/28/2021] [Accepted: 10/29/2021] [Indexed: 01/12/2023] Open
Abstract
NLR family pyrin domain-containing 3 (NLRP3) is an intracellular protein that after recognizing a broad spectrum of stressors, such as microbial motifs and endogenous danger signals, promotes the activation and release of the pro-inflammatory cytokines IL-1β and IL-18, thus playing an essential role in the innate immune response. Several blood cell types, including macrophages, dendritic cells, and hematopoietic stem and progenitor cells (HSPCs), express NLRP3, where it has been implicated in various physiological and pathological processes. For example, NLRP3 participates in the development and expansion of HSPCs, and their release from bone marrow into the peripheral blood has been implicated in certain hematological disorders including various types of leukemia. In addition, accumulating evidence indicates that activation of NLRP3 plays a pivotal role in the development of transplant complications in patients receiving hematopoietic stem cell transplantation (HSCT) including graft versus host disease, severe infections, and transplant-related mortality. The majority of these complications are triggered by the severe tissue damage derived from the conditioning regimens utilized in HSCT which, in turn, activates NLRP3 and, ultimately, promotes the release of proinflammatory cytokines such as IL-1β and IL-18. Here, we summarize the implications of NLRP3 in HSCT with an emphasis on the involvement of this inflammasome component in transplant complications.
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