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      Bioinformatics Analysis Identifies Key Genes and Pathways in Acute Myeloid Leukemia Associated with DNMT3A Mutation

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          DNA methyltransferase 3 alpha (DNMT3A) mutation was one of the most frequent genetic alterations in acute myeloid leukemia (AML), which was associated with poor prognosis and appeared to be a potential biomarker. Herein, we aimed to identify the key genes and pathways involved in adult AML with DNMT3A mutations and to find possible therapeutic targets for improving treatment.


          The RNA sequencing datasets of 170 adult AML patients were obtained from The Cancer Genome Atlas (TCGA) database. EdgeR of the R platform was used to identify the differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by Metascape and DAVID. And protein-protein interaction (PPI) network and clustering modules were analyzed with the STRING database and Cytoscape software.


          Mutated DNMT3A resulted in a shorter overall survival (OS) in AML patients and obviously associated with age, blast percentage in peripheral blood, and FLT3 mutation. A total of 283 DEGs were detected, of which 95 were upregulated and 188 were downregulated. GO term analysis showed that DEGs were significantly enriched in neutrophil degranulation, myeloid cell differentiation, stem cell proliferation, positive regulation of neurological system process, leukocyte migration, and tissue morphogenesis. KEGG pathway enrichment analysis indicated that the pathway of cancer, PI3K-Akt signaling pathway, and transcriptional misregulation in cancer may play a crucial role in DNMT3A mutation AML. Seven hub genes (BMP4, MPO, THBS1, APP, ELANE, HOXA7, and VWF) had a significant prognostic value.


          Bioinformatics analysis in the present study provided novel targets for early diagnosis and new strategies for treatment for AML with DNMT3A mutation.

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          Most cited references 47

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          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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            edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

            Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site ( Contact:
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              Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

              DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.

                Author and article information

                Biomed Res Int
                Biomed Res Int
                BioMed Research International
                23 November 2020
                : 2020
                1Department of Hematology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510000, China
                2Department of Urology & Minimally Invasive Surgery Center, The First Affiliated Hospital of Guangzhou Medical University, Guangdong Key Laboratory of Urology, Institute of Urology, Guangzhou, Guangdong, China
                3Department of Pediatrics, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510000, China
                Author notes

                Academic Editor: Hesham H. Ali

                Copyright © 2020 Shuyi Chen et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Funded by: Natural Science Foundation of Guangdong Province
                Award ID: 2017A030313858
                Funded by: Guangzhou Medical and Health Technology Program
                Award ID: 20181A011062
                Funded by: National Natural Science Foundation of China
                Award ID: 81870113
                Award ID: 81672661
                Research Article


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