Linjun Hu1, Yuling Gao2, Zhan Shi3, Yang Liu1, Junjun Zhao4, Zunqiang Xiao3, Jiayin Lou5, Qiuran Xu6, Xiangmin Tong6. 1. The Medical College of Qingdao University, Qingdao 266071, China. 2. Department of Genetic Laboratory, Shaoxing Women and Children Hospital, Shaoxing 312030, China. 3. The Second Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou 310014, China. 4. Graduate Department, Bengbu Medical College, Bengbu 233030, China. 5. Department of Clinical Laboratory, Da jiang dong Hospital, Hangzhou, 310014, China. 6. The Key Laboratory of Tumor Molecular Diagnosis and Individualized Medicine of Zhejiang Province, Zhejiang Provincial People's Hospital (People's Hospital of Hangzhou Medical College), Hangzhou 310014, China.
Abstract
BACKGROUND: Acute myeloid leukemia (AML) is a heterogeneous clonal disease that prevents normal myeloid differentiation with its common features. Its incidence increases with age and has a poor prognosis. Studies have shown that DNA methylation and abnormal gene expression are closely related to AML. METHODS: The methylation array data and mRNA array data are from the Gene Expression Omnibus (GEO) database. Through the GEO data, we identified differential genes from tumors and normal samples. Then we performed Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses on these differential genes. Protein-protein interaction (PPI) network construction and module analysis were performed to screen the highest-scoring modules. Next, we used SurvExpress software to analyze the genes in the highest-scoring module and selected potential prognostic genes by univariate and multivariate Cox analysis. Finally, the three genes screened by SurvExpress software were analyzed using the methylation analysis site MethSurv to explore AML associated methylation biomarkers. RESULTS: We found three genes that can be used as independent prognostic factors for AML. These three genes are the low expression/methylation genes ATP11A and ITGAM, and the high expression/low methylation gene ZNRF2. CONCLUSIONS: In this study, we performed a comprehensive analysis of DNA methylation and gene expression to identify key epigenetic genes in AML. 2019 Annals of Translational Medicine. All rights reserved.
BACKGROUND: Acute myeloid leukemia (AML) is a heterogeneous clonal disease that prevents normal myeloid differentiation with its common features. Its incidence increases with age and has a poor prognosis. Studies have shown that DNA methylation and abnormal gene expression are closely related to AML. METHODS: The methylation array data and mRNA array data are from the Gene Expression Omnibus (GEO) database. Through the GEO data, we identified differential genes from tumors and normal samples. Then we performed Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses on these differential genes. Protein-protein interaction (PPI) network construction and module analysis were performed to screen the highest-scoring modules. Next, we used SurvExpress software to analyze the genes in the highest-scoring module and selected potential prognostic genes by univariate and multivariate Cox analysis. Finally, the three genes screened by SurvExpress software were analyzed using the methylation analysis site MethSurv to explore AML associated methylation biomarkers. RESULTS: We found three genes that can be used as independent prognostic factors for AML. These three genes are the low expression/methylation genes ATP11A and ITGAM, and the high expression/low methylation gene ZNRF2. CONCLUSIONS: In this study, we performed a comprehensive analysis of DNA methylation and gene expression to identify key epigenetic genes in AML. 2019 Annals of Translational Medicine. All rights reserved.
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