Single nucleotide polymorphisms (SNPs) in CEBPA gene have been found tobe associated with cancer especially Acute Myeloid Leukemia (AML).Therefore, the identification of functional and structural polymorphisms inCEBPA is important to study and discover therapeutics targets and potentialmalfunctioning. For this purpose, several bioinformatics tools were used forthe identification of disease-associated nsSNPs, which might be vital for thestructure and function of CEBPA, making them extremely important. Insilico tools used in this study included SIFT, PROVEAN, PolyPhen2, SNP&GOand PhD-SNP,followed by ConSurf and I-Mutant. Protein 3D modelling wascarried out using I-TASSER and MODELLER v9.22, while GeneMANIA andstring were used forthe 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 mostcrucial SNPs. Additionally, the gene-gene interaction showed the geneshaving correlation with CEBPA's co-expressions and importance in severalpathways. In future, these 11 mutations should be investigated whilestudying diseases related to CEBPA, especially for AML. Being the first of itskind, future perspectives are proposed in this study, which will help inprecision medicine. Animal models are of great significance in finding outCEBPA effects in disease.
Keywords: CEBPA; in silico; AML; nsSNPs;mutations