In-silico analysis of non-synonymous-SNPs of STEAP2: To provoke the progression of prostate cancer

Abstract

AbstractAs a novel biomarker from the STEAP family, STEAP2 encodes six transmembrane epithelial antigens to prostate cancer. The overexpression of STEAP2 is predicted as the second most common cancer in the world that is responsible for male cancer-related deaths. Nonsynonymous SNPs are important group of SNPs which lead to alternations in encoded polypeptides. Changes in the amino acid sequence of gene products can lead to abnormal tissue function. The present study firstly sorted out those SNPs which exist in the coding region of STEAP2 and evaluated their impact through computational tools. Secondly, the three-dimensional structure of STEAP2 was formed through I-TASSER and validated by different software. Genomic data has been retrieved from the 1000 Genome project and Ensembl and subsequently analysed using computational tools. Out of 177 non-synonymous single nucleotide polymorphisms (nsSNPs) within the coding region, 42 mis-sense SNPs have been predicted as deleterious by all analyses. Our research shows a welldesigned computational methodology to inspect the prostate cancer associated nsSNPs. It can be concluded that these nsSNPs can play their role in the up-regulation of STEAP2 which further leads to progression of prostate cancer. It can benefit scientists in the handling of cancerassociated diseases related to STEAP2 through developing novel drug therapies.