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Dove Medical Press

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

Overview of attention for article published in OncoTargets and therapy, December 2017
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43 Mendeley
Title
Identification of key pathways and genes in TP53 mutation acute myeloid leukemia: evidence from bioinformatics analysis
Published in
OncoTargets and therapy, December 2017
DOI 10.2147/ott.s156003
Pubmed ID
Authors

Rui Huang, Xiwen Liao, Qiaochuan Li

Abstract

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. 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. 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. 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.

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The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 43 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 19%
Student > Bachelor 6 14%
Researcher 5 12%
Student > Master 3 7%
Student > Doctoral Student 2 5%
Other 2 5%
Unknown 17 40%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 26%
Medicine and Dentistry 6 14%
Agricultural and Biological Sciences 5 12%
Social Sciences 2 5%
Neuroscience 1 2%
Other 1 2%
Unknown 17 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 January 2018.
All research outputs
#15,175,718
of 25,382,440 outputs
Outputs from OncoTargets and therapy
#779
of 3,016 outputs
Outputs of similar age
#230,726
of 444,941 outputs
Outputs of similar age from OncoTargets and therapy
#15
of 73 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,016 research outputs from this source. They receive a mean Attention Score of 2.9. This one has gotten more attention than average, scoring higher than 72% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 444,941 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 73 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.