An in-silico study to determine susceptibility to cancer by evaluating the coding and non-coding non-synonymous single nucleotide variants in the gene.

Single Nucleotide Variant (SNVs) affect gene expression as well as protein structure and activity, leading to reduced signaling capabilities and ultimately, increasing cancer risk. SOCS3 (suppressor of cytokine signaling 3), a critical tumor suppressor providing a substantial part in the feedback loop of the JAK/STAT pathway, is abnormally suppressed in various cancer. This study aims to screen non-coding and potentially deleterious coding SNVs in the gene. We performed six programs: PredictSNP1.0 (predicting Deleterious nsSNVs), ConSurf (analyzing sequence conservation), ModPred (analyzing SNVS in PTMs sites), I-Mutant and MUpro (to analyze SNVs effecting protein stability), and molecular docking and molecular dynamics (MD) (to assess the consequences of SOCS3 genetic variations on JAK interactions) for coding regions and three programs (UTRSite, SNP2TFBS, miRNA SNP) (to analyze SNVs effecting the gene expression) in non-coding regions, respectively. After screening 2786 SOCS3 SNVs, we found 10 SNVs, as well as 49 SNPs that change the function of non-coding areas. Out of 10 selected nsSNVs, 3 SNVs (W48R, R71C, N198S) predicted to be the most damaging by all the software programs, as well as one nsSNV (R194W) could be highly deleterious from Molecular Docking analysis combined with MD Simulations. Our findings propose a procedure for studying the structure-related consequences of SNVs on protein function in the future.Communicated by Ramaswamy H. Sarma.