急性髓系白血病免疫评分的建立及验证  

Establishment and Validation of Immune Risk Score for Predicting Survival of Patients with Acute Myeloid Leukemia

作　　者：胡芳 王韵[1,2,3] 张钰[4] 曾云[5] 王顺清[6] 潘学谊[7] 杨同华 刘启发[5] 梁洋[1,2,3] HU Fang;WANG Yun;ZHANG Yu;ZENG Yun;WANG Shun-qing;PAN Xue-Yi;YANG Tong-Hua;LIU Qi-Fa;LIANG Yang(Department of Hematologic Oncology,Sun Yat-sen University Cancer Center,Guangzhou 510060 Guangdong Province,China;State Key Laboratory of Oncology in South China,Guangzhou 510060 Guangdong Province,China;Collaborative Innovation Center for Cancer Medicine,Guangzhou 510060 Guangdong Province,China;Depariment of Hematology,Nanfang Hospital Southern Medical University,Guangzhou 510515,Guangdong Province,China;Department of Hematology,First Affiliated Hospital of Kunming Medical University,Kunming 650504,Yunnan Province,China;Departmentof Hematology,Guangzhou First People's Hospital South China University of Technology,Guangzhou 510030 Guangdong Province,China;Department of Hematology,The First Affiliated Hospital of Guangdong Pharmaceutical University,Guangzhou 510060,Guangdong Province,China;Depariment of Hematology,The First People's Hospital of Yunnan Province,Kunming 650000 Yunnan Province,China)

机构地区：[1]中山大学肿瘤防治中心血液肿瘤科,广东广州510060 [2]中山大学华南肿瘤国家重点实验室,广东广州510060 [3]中山大学肿瘤医学协同创新中心,广东广州510060 [4]南方医科大学南方医院血液科,广东广州510515 [5]昆明医科大学第一附属医院血液科,云南昆明650504 [6]华南理工大学附属广州市第一人民医院血液科,广东广州510030 [7]广东药科大学附属第一医院血液科,广东广州510060 [8]昆明理工大学附属云南省第一人民医院血液科,云南昆明650000

出　　处：《中国实验血液学杂志》2022年第2期327-333,共7页Journal of Experimental Hematology

基　　金：国家自然科学基金(81873428,81660682)。

摘　　要：目的:建立急性髓系白血病(AML)免疫基因预后模型并探索其与骨髓免疫微环境之间的关系。方法:从TCGA数据库下载TCGA-AML基因表达谱以及临床资料数据。通过LASSO分析筛选构建预测模型的免疫基因,模型预测精度通过受试者工作特征曲线和曲线下面积来量化,生存分析采用Log-rank检验。通过基因集富集分析评估不同免疫风险状态下的通路以及功能富集状态,并在真实世界中通过流式分析验证免疫预测模型与骨髓免疫微环境之间的相关性。结果:免疫基因模型风险低的患者预后优于风险高的患者,多因素分析显示,该免疫基因风险模型为独立预后因素。训练集中AML患者的危险比为HR=24.594(95%CI:6.180-97.878),1、3和5年总生存率的AUC分别为0.811、0.815和0.837。此外,差异基因集富集分析提示,细胞因子以及趋化因子等免疫相关通路激活以及自身免疫疾病相关通路激活。同时,真实世界数据显示,免疫风险高的患者CD8+T细胞以及B淋巴细胞数量较低风险患者更低。结论:本研究构建了一个稳定的AML预后评估模型,它不仅可以用来预测AML的预后,还能进一步揭示了免疫微环境的失调。Objective: To establish an immune gene prognostic model of acute myeloid leukemia(AML) and explore its correlation with immune cells in bone marrow microenvironment. Methods: Gene expression profile and clinical data of TCGA-AML were downloaded from TCGA database. Immune genes were screened by LASSO analysis to construct prognosis prediction model, and prediction accuracy of the model was quantified by receiver operating characteristic curve and area under the curve. Survival analysis was performed by Log-rank test. Enriched pathways in the different immune risk subtypes were evaluated from train cohort. The relationship between immune prediction model and bone marrow immune microenvironment was verified by flow cytometry in the real world. Results: Patients with low-risk score of immune gene model had better prognosis than those with high-risk score. Multivariate analysis showed that the immune gene risk model was an independent prognostic factor. The risk ratio for AML patients in the training concentration was HR=24.594(95%CI: 6.180-97.878), and the AUC for 1-year, 3-year, and 5-year overall survival rate was 0.811, 0.815, and 0.837, respectively. In addition, enrichment analysis of differential gene sets indicated activation of immune-related pathways such as cytokines and chemokines as well as autoimmune disease-related pathways. At the same time, real world data showed that patients with high immune risk had lower numbers of CD8+T cells and B lymphocytes compared with low immune risk patients. Conclusion: We constructed a stable prognostic model for AML, which can not only predict the prognosis of AML, but also reveal the dysregulation of immune microenvironment.

关 键 词：急性髓系白血病 免疫风险模型 骨髓微环境 通路富集 

分 类 号：R733.71[医药卫生—肿瘤]