Integrative Analysis of Multi-Omics Identified the Prognostic Biomarkers in Acute Myelogenous Leukemia

被引:19
|
作者
Zheng, Jiafeng [1 ]
Zhang, Tongqiang [1 ]
Guo, Wei [1 ]
Zhou, Caili [2 ]
Cui, Xiaojian [3 ]
Gao, Long [4 ]
Cai, Chunquan [5 ]
Xu, Yongsheng [1 ]
机构
[1] Tianjin Univ, Dept Pediat Resp Med, Childrens Hosp, Tianjin Childrens Hosp, Tianjin, Peoples R China
[2] Tianjin Univ, Dept Sci & Educ, Childrens Hosp, Tianjin Childrens Hosp, Tianjin, Peoples R China
[3] Tianjin Univ, Dept Clin Lab, Childrens Hosp, Tianjin Childrens Hosp, Tianjin, Peoples R China
[4] Tianjin Univ, Dept Pediat Endocrinol, Childrens Hosp, Tianjin Childrens Hosp, Tianjin, Peoples R China
[5] Tianjin Univ, Tianjin Inst Pediat, Tianjin Key Lab Birth Defects Prevent & Treatment, Tianjin Childrens Hosp,Childrens Hosp, Tianjin, Peoples R China
来源
FRONTIERS IN ONCOLOGY | 2020年 / 10卷
基金
中国国家自然科学基金;
关键词
multi-omics; prognostic; acute myelogenous leukemia; children; methylation; ABERRANT DNA METHYLATION; EXPRESSION; CANCER; TUMOR;
D O I
10.3389/fonc.2020.591937
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Background Acute myelogenous leukemia (AML) is a common pediatric malignancy in children younger than 15 years old. Although the overall survival (OS) has been improved in recent years, the mechanisms of AML remain largely unknown. Hence, the purpose of this study is to explore the differentially methylated genes and to investigate the underlying mechanism in AML initiation and progression based on the bioinformatic analysis. Methods Methylation array data and gene expression data were obtained from TARGET Data Matrix. The consensus clustering analysis was performed using ConsensusClusterPlus R package. The global DNA methylation was analyzed using methylationArrayAnalysis R package and differentially methylated genes (DMGs), and differentially expressed genes (DEGs) were identified using Limma R package. Besides, the biological function was analyzed using clusterProfiler R package. The correlation between DMGs and DEGs was determined using psych R package. Moreover, the correlation between DMGs and AML was assessed using varElect online tool. And the overall survival and progression-free survival were analyzed using survival R package. Results All AML samples in this study were divided into three clusters at k = 3. Based on consensus clustering, we identified 1,146 CpGs, including 40 hypermethylated and 1,106 hypomethylated CpGs in AML. Besides, a total 529 DEGs were identified, including 270 upregulated and 259 downregulated DEGs in AML. The function analysis showed that DEGs significantly enriched in AML related biological process. Moreover, the correlation between DMGs and DEGs indicated that seven DMGs directly interacted with AML. CD34, HOXA7, and CD96 showed the strongest correlation with AML. Further, we explored three CpG sites cg03583857, cg26511321, cg04039397 of CD34, HOXA7, and CD96 which acted as the clinical prognostic biomarkers. Conclusion Our study identified three novel methylated genes in AML and also explored the mechanism of methylated genes in AML. Our finding may provide novel potential prognostic markers for AML.
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页数:12
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