Twenty novel nsSNPs may affect FLT3 gene leading to Acute Myeloid Leukemia (AML) using in silico analysis

Alsheikh, T.; Ameer, T.; NjmEldin, A.; Omer, D.; Agbash, A.; Abdalmonem, Z.; Suliman, H.; Hamad, H.; A. ayoub, S. E.; A. Hassan, M.

doi:10.1101/2023.06.24.546344

bioRxiv · 10.1101/2023.06.24.546344

Twenty novel nsSNPs may affect FLT3 gene leading to Acute Myeloid Leukemia (AML) using in silico analysis

Alsheikh, T.·Ameer, T.·NjmEldin, A.·Omer, D.·Agbash, A.·Abdalmonem, Z.·Suliman, H.·Hamad, H.·A. ayoub, S. E.·A. Hassan, M.

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Abstract

Background: Mutations within the FMS-like tyrosine kinase 3 (FLT3) gene represent one of the most common genetic alteration that disturb intracellular signaling networks with a key role in leukemia pathogenesis. laboratory studies considerable obstacle to identify functional SNPs in a specific gene. Thus, the "in silico" technique is possible now to carry out research investigations without the need for extensive lab work. Methodology: data retrieved from NCBI database and different algorithm used to analyse nsSNPs which they are: SIFT, Polyphen-2, Provean, SNAP2, P-Mut, I-Mutant, Project Hope, Raptor X, PolymiRTS and Gene MANIA. Result: Our study reveals twenty novel SNPs regarded to be the most damaging SNPs that affect structure and function of FLT3 gene using different bioinformatics algorithm. Conclusion: This study revealed 20 damaging SNPs considered to be novel nsSNP in FLT3 gene that leads to AML, by using different algorithms. Additionally, 69 functional classes were predicted in 12 SNPs in the 3UTR, among them, 31 alleles disrupted a conserved miRNA site and 37 derived alleles created a new site of miRNA. This might result in the de regulation of the gene function. These results could be valuable for molecular studying, diagnosis and treatment of AML patients.

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Alsheikh, T., Ameer, T., NjmEldin, A., Omer, D., Agbash, A., Abdalmonem, Z., Suliman, H., Hamad, H., A. ayoub, S. E., A. Hassan, M.. 2023-06-26. Twenty novel nsSNPs may affect FLT3 gene leading to Acute Myeloid Leukemia (AML) using in silico analysis. https://doi.org/10.1101/2023.06.24.546344

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