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Cancer Inform. 2019 Aug 19;18:1176935119870817. doi: 10.1177/1176935119870817. eCollection 2019.

In Silico Genetics Revealing 5 Mutations in CEBPA Gene Associated With Acute Myeloid Leukemia.

Author information

1
Department of Biotechnology, Africa City of Technology, Khartoum North, Sudan.
2
Department of Haematology, Ribat University Hospital, Khartoum, Sudan.

Abstract

Background:

Acute myeloid leukemia (AML) is an extremely heterogeneous malignant disorder; AML has been reported as one of the main causes of death in children. The objective of this work was to classify the most deleterious mutation in CCAAT/enhancer-binding protein-alpha (CEBPA) and to predict their influence on the functional, structural, and expression levels by various Bioinformatics analysis tools.

Methods:

The single nucleotide polymorphisms (SNPs) were claimed from the National Center for Biotechnology Information (NCBI) database and then submitted into various functional analysis tools, which were done to predict the influence of each SNP, followed by structural analysis of modeled protein followed by predicting the mutation effect on energy stability; the most damaging mutations were chosen for additional investigation by Mutation3D, Project hope, ConSurf, BioEdit, and UCSF Chimera tools.

Results:

A total of 5 mutations out of 248 were likely to be responsible for the structural and functional variations in CEBPA protein, whereas in the 3'-untranslated region (3'-UTR) the result showed that among 350 SNPs in the 3'-UTR of CEBPA gene, about 11 SNPs were predicted. Among these 11 SNPs, 65 alleles disrupted a conserved miRNA site and 22 derived alleles created a new site of miRNA.

Conclusions:

In this study, the impact of functional mutations in the CEBPA gene was investigated through different bioinformatics analysis techniques, which determined that R339W, R288P, N292S, N292T, and D63N are pathogenic mutations that have a possible functional and structural influence, therefore, could be used as genetic biomarkers and may assist in genetic studies with a special consideration of the large heterogeneity of AML.

KEYWORDS:

Acute myeloid leukemia; CEBPA; bioinformatics analysis; genetic biomarkers; malignant disease

PMID:
31621694
PMCID:
PMC6777061
DOI:
10.1177/1176935119870817

Conflict of interest statement

Declaration of conflicting interest:The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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