In silico SNP analysis of the breast cancer antigen NY-BR-1

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

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.

Peptide-Based Vaccines in Clinical Phases and New Potential Therapeutic Targets as a New Approach for Breast Cancer: A Review

Breast cancer is the leading cause of death in women from 20 to 59 years old. The conventional treatment includes surgery, chemotherapy, hormonal therapy, and immunotherapy. This immunotherapy is based on administering monoclonal therapeutic antibodies (passive) or vaccines (active) with therapeutic purposes. Several types of vaccines could be used as potential treatments for cancer, including whole-cell, DNA, RNA, and peptide-based vaccines. Peptides used to develop vaccines are derived from tumor-associated antigens or tumor-specific antigens, such as HER-2, MUC1, ErbB2, CEA, FRα, MAGE A1, A3, and A10, NY-ESO-1, among others. Peptide-based vaccines provide some advantages, such as low cost, purity of the antigen, and the induction of humoral and cellular immune response. In this review, we explore the different types of vaccines against breast cancer with a specific focus on the description of peptide-based vaccines, their composition, immune response induction, and the description of new potential therapeutic targets.

Frequent Molecular Subtype Switching and Gene Expression Alterations in Lung and Pleural Metastasis From Luminal A-Type Breast Cancer

PURPOSE

Conversion of tumor subtype frequently occurs in the course of metastatic breast cancer but is a poorly understood phenomenon. This study aims to compare molecular subtypes with subsequent lung or pleural metastasis.

PATIENTS AND METHODS

In a cohort of 57 patients with breast cancer and lung or pleural metastasis (BCLPM), we investigated paired primary and metastatic tissues for differential gene expression of 269 breast cancer genes. The PAM50 classifier was applied to identify intrinsic subtypes, and differential gene expression and cluster analysis were used to further characterize subtypes and tumors with subtype conversion.

RESULTS

In primary breast cancer, the most frequent molecular subtype was luminal A (lumA; 49.1%); it was luminal B (lumB) in BCLPM (38.6%). Subtype conversion occurred predominantly in lumA breast cancers compared with other molecular subtypes (57.1% v 27.6%). In lumA cancers, 62 genes were identified with differential expression in metastatic versus primary disease, compared with only 10 differentially expressed genes in lumB, human epidermal growth factor receptor 2 (HER2)–enriched, and basal subtypes combined. Gene expression changes in lumA cancers affected not only the repression of the estrogen receptor pathway and cell cycle–related genes but also the WNT pathway, proteinases (MME, MMP11), and motility-associated cytoskeletal proteins (CK5, CK14, CK17). Subtype-switched lumA cancers were further characterized by cell proliferation and cell cycle checkpoint gene upregulation and dysregulation of the p53 pathway. This involved 83 notable gene expression changes.

CONCLUSION

Our results indicate that gene expression changes and subsequent subtype conversion occur on a large scale in metastatic luminal A–type breast cancer compared with other molecular subtypes. This underlines the significance of molecular changes in metastatic disease, especially in tumors of initially low aggressive potential.

In silico analysis of nonsynonymous single-nucleotide polymorphisms (nsSNPs) of the SMPX gene

Mutations in the SMPX gene can disrupt the regular activity of the SMPX protein, which is involved in the hearing process. Recent reports showing a link between nonsynonymous single-nucleotide polymorphisms (nsSNPs) in SMPX and hearing loss, thus classifying deleterious SNPs in SMPX will be an uphill task before designing a more extensive population study. In this study, damaging nsSNPs of SMPX from the dbSNP database were identified by using 13 bioinformatics tools. Initially, the impact of nsSNPs in the SMPX gene were evaluated through different in silico predictors; and the deleterious convergent changes were analyzed by energy-minimization-guided residual network analysis. In addition, the pathogenic effects of mutations in SMPX-mediated protein-protein interactions were also characterized by structural modeling and binding energy calculations. A total of four mutations (N19D, A29T, K54N, and S71L) were found to be highly deleterious by all the tools, which are located at highly conserved regions. Furthermore, all four mutants showed structural alterations, and the communities of amino acids for mutant proteins were readily changed, compared to the wild-type. Among them, A29T (rs772775896) was revealed as the most damaging nsSNP, which caused significant structural deviation of the SMPX protein, as a result reducing the binding affinity to other functional partners. These findings reflect the computational insights into the deleterious role of nsSNPs in SMPX, which might be helpful for subjecting wet-lab confirmatory analysis.

Identification and structural characterization of deleterious non-synonymous single nucleotide polymorphisms in the human SKP2 gene

In SCF (Skp, Cullin, F-box) ubiquitin-protein ligase complexes, S-phase kinase 2 (SKP2) is one of the major players of F-box family, that is responsible for the degradation of several important cell regulators and tumor suppressor proteins. Despite of having significant evidence for the role of SKP2 on tumorgenesis, there is a lack of available data regarding the effect of non-synonymous polymorphisms. In this communication, the structural and functional consequences of non-synonymous single nucleotide polymorphisms (nsSNPs) of SKP2 have been reported by employing various computational approaches and molecular dynamics simulation. Initially, several computational tools like SIFT, PolyPhen-2, PredictSNP, I-Mutant 2.0 and ConSurf have been implicated in this study to explore the damaging SNPs. In total of 172 nsSNPs, 5 nsSNPs were identified as deleterious and 3 of them were predicted to be decreased the stability of protein. Guided from ConSurf analysis, P101L (rs761253702) and Y346C (rs755010517) were categorized as the highly conserved and functional disrupting mutations. Therefore, these mutations were subjected to three dimensional model building and molecular dynamics simulation study for the detailed structural consequences upon the mutations. The study revealed that P101L and Y346C mutations increased the flexibility and changed the structural dynamics. As both these mutations are located in the most functional regions of SKP2 protein, these computational insights might be helpful to consider these nsSNPs for wet-lab confirmatory analysis as well as in rationalizing future population based studies and structure based drug design against SKP2.