| Summary: | Objectives: Cancer and diabetes are two diseases known to bypass socio-demographic segregation, hence the increased focus on their molecular mechanism for therapeutic intervention. Their connection was unclear using traditional biomedical methods until omics technology shows how specific aetiological factors seem to contribute to their initiation and progression. The Genome-wide association study (GWAS) was considered a ground-breaking tool which highlights the roles specific genetic variations like single nucleotide polymorphisms (SNPs) play in the surveillance, diagnosis and treatment of diseases like cancer and diabetes. Methods: While some common missense SNP variants have been investigated and reported, we used bioinformatics tools to predict all known conditions previously reported to link pancreatic cancer and diabetes mellitus subtypes based on metabolism and cell proliferation for their structural and functional significance. The variants were limited to five known genes: epigenetic regulators (EZH2, SIRT1) and pan-peroxisome proliferator-activated receptors (PPARA, PPARD, PPARG). Results: The class filter shows that obesity is a common risk factor. In the result projected, six missense variant rs1232898090, rs1800206, rs1801282, rs1805192 rs121909244 and rs777334819 were discovered to be associated with fifteen different deleterious amino acid point change in the PPARA and PPARG genes. Likewise, the phyre 2 and SwissModel homology modelling analysis revealed proteins with an identifier; 2FVJ, 2P54, 3DZU, 3E00, 4BCR and 6FZG to be common with high alignment coverage and percentage identity for the missense SNP variants. Conclusion: Overall, this analysis provides important insights into how genetic variants can be selected, screened and proposed to inform target selection for other downstream insilico and experimental validation, significant in developing new therapeutics for cancer and diabetes.
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