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Identification of key pathways and genes in TP53 mutation acute myeloid leukemia: evidence from bioinformatics analysis

Authors Huang R, Liao X, Li Q

Received 3 November 2017

Accepted for publication 5 December 2017

Published 28 December 2017 Volume 2018:11 Pages 163—173


Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 3

Editor who approved publication: Dr Yao Dai

Rui Huang,1,* Xiwen Liao,2,* Qiaochuan Li1

1Department of Hematology, 2Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People’s Republic of China

*These authors contributed equally to this work

Background: Tumor protein p53 (TP53) mutations are not only a risk factor in acute myeloid leukemia (AML) but also a potential biomarker for individualized treatment options. This study aimed to investigate potential pathways and genes associated with TP53 mutations in adult de novo AML.
Methods: An RNA sequencing dataset of adult de novo AML was downloaded from The Cancer Genome Atlas database. Differentially expressed genes (DEGs) were identified by edgeR of the R platform. Key pathways and genes were identified using the following bioinformatics tools: gene set enrichment analysis (GSEA), gene ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), Search Tool for the Retrieval of Interacting Genes/Proteins, and Molecular Complex Detection.
Results: GSEA suggested that TP53 mutations were significantly associated with cell differentiation, proliferation, cell adhesion biological processes, and MAPK pathway. In total, 1,287 genes were identified as DEGs. GO and KEGG analysis suggested that upregulation of DEGs was significantly enriched in categories associated with cell adhesion biological processes, Ras-associated protein 1, PI3K–Akt pathway, and cell adhesion molecules. The top ten genes ranked by degree, CDH1, BMP2, KDR, LEP, CASR, ITGA2B, APOE, MNX1, NMU, and TRH, were identified as hub genes from the protein–protein interaction network. Survival analysis suggested that patients with TP53 mutations had a significantly increased risk of death, while the mRNA expression level in patients with TP53 mutation was similar to those carrying TP53 wild type.
Conclusion: Our findings have indicated that multiple genes and pathways may play a crucial role in TP53 mutation AML, offering candidate targets and strategies for TP53 mutation AML individualized treatment.

Keywords: bioinformatics analysis, acute myeloid leukemia, TCGA, RNA sequencing, TP53 mutation

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