COMPUTATIONAL CHARACTERIZATION OF NON-SYNONYMOUS SNPS OF THE HUMAN PROSTATE CANCER-ASSOCIATED EHBP1 GENE

Abstract

Prostate cancer is one of the frequently reported cancers in men worldwide, and it is the third most prevalent urogenital cancer among males in the Pakistani population. Variations in several oncogenes and tumor suppressor genes have been studied that play a role in prostate cancer. GWAS has identified SNPs in the EHBP1 gene to be associated with prostate cancer. In this study, we used in-silico approaches for identifying the most damaging non-synonymous SNPs (nsSNPs), playing a significant structural and functional role in EHBP1 protein. Data on non-synonymous SNPs were recruited from Ensembl. Deleterious nsSNPs were identified using SIFT, PolyPhen2, PhD-SNP, fathmm, and SNPs&GO. Structural, functional, stability analysis and conservation profile of nsSNPs were verified using MutPred, I-Mutant, MUpro and ConSurf web server, respectively. STRING was used for protein-protein interaction. GeneMANIA was utilized to check the EHBP1 gene interaction with other genes. The 3D structures of wild-type and mutant proteins were generated using I-Tasser. Post-translational modification sites were predicted through the MusuitDeep web server. Our study identified 32 most damaging nsSNPs in the EHBP1 gene. Structural and functional analysis of these nsSNPs manifests that they have a deleterious effect on protein structure and function. Stability analysis showed that 31 of these nsSNPs decrease protein stability and are located in highly conserved regions. Gene-gene interactions revealed a relationship between EHBP1 and other genes, highlighting its significance in multiple pathways and co-expression patterns. In future, these 32 SNPs provide suitable target variants to be explored, through population-based studies, in diseases associated with the EHBP1 gene, such as prostate cancer, as well as for their role as novel biomarkers.