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. 2020 Dec 10:10:591937.
doi: 10.3389/fonc.2020.591937. eCollection 2020.

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

Jiafeng Zheng  1 , Tongqiang Zhang  1 , Wei Guo  1 , Caili Zhou  2 , Xiaojian Cui  3 , Long Gao  4 , Chunquan Cai  5 , Yongsheng Xu  1
Affiliations

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

Jiafeng Zheng et al. Front Oncol. .

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.

Keywords: acute myelogenous leukemia; children; methylation; multi-omics; prognostic.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Unsupervised clustering analysis in AML. (A) The empirical cumulative distribution function (CDF) plots revealed the consensus distributions for each k. (B) The delta area score displayed the relative growth in cluster stability. (C) The circular manhattan (CM) plot exhibited the clusters at k = 3. (D) The overall survival (OS) plot for the AML patients of clusters 1, 2, 3 using survival R package. (B) progression-free survival (PFS) plot for the AML patients of clusters 1, 2, 3 using survival R package.
Figure 2
Figure 2
Global methylation analysis. (A) The principal component analysis (PCA) for the sample clustering. (B) The methylation density distribution plot showed the sample distribution trend and repeatability. (C) The volcano plot displayed the differential CpG sites between cluster 3 and cluster 2. (D–F) The methylated heatmap of the WT1, RUNX3, and CCND1.
Figure 3
Figure 3
Identification of the DEGs in AML. (A) The PCA for the sample clustering. (B) The volcano plot displayed the DEGs between cluster 3 and cluster 2. (C) The heatmap exhibited the DEGs between cluster 3 and cluster 2.
Figure 4
Figure 4
Function analysis of DEGs. GO annotation (A) and (D) BP, (B) and (E) CC, (F) MF, and (C) and (G) KEGG enrichment analysis the enriched pathways of upregulated/downregulated DEGs. (H) The transcriptional misregulation in cancer pathway (hsa052202) was enriched by KEGG analysis. (I) The acute myeloid leukemia pathways (hsa05221) were enriched by KEGG analysis.
Figure 5
Figure 5
Correlation between DMG sites and DEG analysis. The co-expression network of DMGs and DEGs.
Figure 6
Figure 6
Clinical prognostic biomarker analysis. (A–C) The ROC plots of CD34, CD96, and HOXA7 for the consensus clusters. (D) The circular plot of CpGs.
See this image and copyright information in PMC

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