Jiafeng Zheng1, Tongqiang Zhang1, Wei Guo1, Caili Zhou2, Xiaojian Cui3, Long Gao4, Chunquan Cai5, Yongsheng Xu1. 1. Department of Pediatric Respiratory Medicine, Tianjin Children's Hospital (Tianjin University Children's Hospital), Tianjin, China. 2. Department of Science and Education, Tianjin Children's Hospital (Tianjin University Children's Hospital), Tianjin, China. 3. Department of Clinical Lab, Tianjin Children's Hospital (Tianjin University Children's Hospital), Tianjin, China. 4. Department of Pediatric Endocrinology, Tianjin Children's Hospital (Tianjin University Children's Hospital), Tianjin, China. 5. Tianjin Institute of Pediatrics (Tianjin Key Laboratory of Birth Defects for Prevention and Treatment), Tianjin Children's Hospital (Tianjin University Children's Hospital), Tianjin, China.
Abstract
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.
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.
Authors: Hamid Bolouri; Jason E Farrar; Timothy Triche; Rhonda E Ries; Emilia L Lim; Todd A Alonzo; Yussanne Ma; Richard Moore; Andrew J Mungall; Marco A Marra; Jinghui Zhang; Xiaotu Ma; Yu Liu; Yanling Liu; Jaime M Guidry Auvil; Tanja M Davidsen; Patee Gesuwan; Leandro C Hermida; Bodour Salhia; Stephen Capone; Giridharan Ramsingh; Christian Michel Zwaan; Sanne Noort; Stephen R Piccolo; E Anders Kolb; Alan S Gamis; Malcolm A Smith; Daniela S Gerhard; Soheil Meshinchi Journal: Nat Med Date: 2018-04-10 Impact factor: 53.440
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