An in-silico study to determine susceptibility to cancer by evaluating the coding and non-coding non-synonymous single nucleotide variants in the SOCS3 gene

Sadri Fatemeh; Zarei Mahboobeh; Ahmadi Khadijeh; Mohajeri Khorasani Amirhossein; Mousavi Pegah

doi:10.1080/07391102.2023.2256408

An in-silico study to determine susceptibility to cancer by evaluating the coding and non-coding non-synonymous single nucleotide variants in the SOCS3 gene

J Biomol Struct Dyn. 2023 Sep 27:1-12. doi: 10.1080/07391102.2023.2256408. Online ahead of print.

Authors

Sadri Fatemeh¹, Zarei Mahboobeh¹, Ahmadi Khadijeh², Mohajeri Khorasani Amirhossein³, Mousavi Pegah¹

Affiliations

¹ Molecular Medicine Research Center, Hormozgan Health Institute, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.
² Infectious and Tropical Diseases Research Center, Hormozgan Health Institute, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.
³ Student Research Committee, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.

PMID: 37753777
DOI: 10.1080/07391102.2023.2256408

Abstract

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 SOCS3 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.

Keywords: SNVs; SOCS3; bioinformatics tools; cancer; genetic variation; tumor suppressor.

Plain language summary

Genetic variation information allows for helpful knowledge into a gene’s functional spectrum and specific domains. This is the first study to identify several prioritizing deleterious SNVs in coding and non-coding regions of the SOCS3 gene, a key tumor suppressor, using a variety of computational tools to identify the critical genetic alterations that contribute to the beginning and progress of cancer. This result could be helpful in the clinical diagnosis and management of future cancer treatments.