Zheng Jiafeng, Zhang Tongqiang, Guo Wei, Zhou Caili, Cui Xiaojian, Gao Long, Cai Chunquan, Xu Yongsheng
Department of Pediatric Respiratory Medicine, Tianjin Children's Hospital (Tianjin University Children's Hospital), Tianjin, China.
Department of Science and Education, Tianjin Children's Hospital (Tianjin University Children's Hospital), Tianjin, China.
Front Oncol. 2020 Dec 10;10:591937. doi: 10.3389/fonc.2020.591937. eCollection 2020.
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.
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.
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.
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.
急性髓系白血病（AML）是15岁以下儿童常见的儿科恶性肿瘤。尽管近年来总体生存率（OS）有所提高，但AML的发病机制仍 largely未知。因此，本研究的目的是基于生物信息学分析探索差异甲基化基因，并研究AML发生和发展的潜在机制。
从TARGET数据矩阵获取甲基化阵列数据和基因表达数据。使用ConsensusClusterPlus R包进行共识聚类分析。使用methylationArrayAnalysis R包分析全基因组DNA甲基化，并使用Limma R包鉴定差异甲基化基因（DMG）和差异表达基因（DEG）。此外，使用clusterProfiler R包分析生物学功能。使用psych R包确定DMG和DEG之间的相关性。此外，使用varElect在线工具评估DMG与AML之间的相关性。并使用survival R包分析总生存期和无进展生存期。
本研究中的所有AML样本在k = 3时分为三个聚类。基于共识聚类，我们在AML中鉴定出1146个CpG，包括40个高甲基化和1106个低甲基化的CpG。此外，共鉴定出529个DEG，包括AML中270个上调和259个下调的DEG。功能分析表明，DEG在AML相关生物学过程中显著富集。此外，DMG与DEG之间的相关性表明，7个DMG与AML直接相互作用。CD34、HOXA7和CD96与AML的相关性最强。此外，我们探索了CD34、HOXA7和CD96的三个CpG位点cg03583857、cg26511321、cg04039397，它们作为临床预后生物标志物。
我们的研究在AML中鉴定出三个新的甲基化基因，并探索了甲基化基因在AML中的机制。我们的发现可能为AML提供新的潜在预后标志物。
