Establishment and Validation of Immune Risk Score for Predicting Survival of Patients with Acute Myeloid Leukemia.
10.19746/j.cnki.issn.1009-2137.2022.02.001
- Author:
Fang HU
1
,
2
,
3
;
Yun WANG
1
,
2
,
3
;
Yu ZHANG
4
;
Yun ZENG
5
;
Shun-Qing WANG
6
;
Xue-Yi PAN
7
;
Tong-Hua YANG
8
;
Qi-Fa LIU
9
;
Yang LIANG
1
,
2
,
10
Author Information
1. Department of Hematologic Oncology, Sun Yat-sen University Cancer Center,Guangzhou 510060, Guangdong Province, China
2. State Key Laboratory of Oncology in South China, Guangzhou 510060, Guangdong Province, China
3. Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, Guangdong Province, China.
4. Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, Guangdong Province, China.
5. Department of Hematology, First Affiliated Hospital of Kunming Medical University, Kunming 650504, Yunnan Province, China.
6. Department of Hematology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou 510030, Guangdong Province, China.
7. Department of Hematology, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou 510060, Guangdong Province, China.
8. Department of Hematology, The First People's Hospital of Yunnan Province, Kunming 650000, Yunnan Province, China.
9. Department of Hematology, First Affiliated Hospital of Kunming Medical University, Kunming 650504, Yunnan Province, China,E-mail: liuqifa628@163.com.
10. Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, Guangdong Province, China,E-mail: liangyang@sysucc.org.cn.
- Publication Type:Journal Article
- Keywords:
acute myeloid leukemia;
bone marrow microenvironment;
immune gene model;
pathway enrichment
- MeSH:
Humans;
Leukemia, Myeloid, Acute/genetics*;
Prognosis;
ROC Curve;
Risk Factors;
Transcriptome;
Tumor Microenvironment/genetics*
- From:
Journal of Experimental Hematology
2022;30(2):327-333
- CountryChina
- Language:Chinese
-
Abstract:
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.
