In Silico Identification of Novel Acute Myeloid Leukemia Associated Missense SNPs in Human CEBPA Gene

Abstract

Single nucleotide polymorphisms (SNPs) in CEBPA gene have been found to be associated with cancer especially Acute Myeloid Leukemia (AML). Therefore, the identification of functional and structural polymorphisms in CEBPA is important to study and discover therapeutics targets and potential malfunctioning. For this purpose, several bioinformatics tools were used for the identification of disease-associated nsSNPs, which might be vital for the structure and function of CEBPA, making them extremely important. In silico tools used in this study included SIFT, PROVEAN, PolyPhen2, SNP&GO and PhD-SNP, followed by ConSurf and I-Mutant. Protein 3D modelling was carried out using I-TASSER and MODELLER v9.22, while GeneMANIA and string were used for the prediction of gene-gene interaction in this regard. From our study, we found that the L345P, R333C, R339Q, V328G, R327W, L317Q, N292S, E284A, R156W, Y108N and F82L mutations were the most crucial SNPs. Additionally, the gene-gene interaction showed the genes having correlation with CEBPA’s co-expressions and importance in several pathways. In future, these 11 mutations should be investigated while studying diseases related to CEBPA, especially for AML. Being the first of its kind, future perspectives are proposed in this study, which will help in precision medicine. Animal models are of great significance in finding out CEBPA effects in disease.