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http://dx.doi.org/10.7314/APJCP.2016.17.4.2199

Prediction and Analysis of Breast Cancer Related Deleterious Non-Synonymous Single Nucleotide Polymorphisms in the PTEN Gene  

Naidu, C Kumaraswamy (Department of Zoology, Sri Venkateswara University)
Suneetha, Y (Department of Zoology, Sri Venkateswara University)
Publication Information
Asian Pacific Journal of Cancer Prevention / v.17, no.4, 2016 , pp. 2199-2203 More about this Journal
Abstract
One of the most common cancer types faced by the women around the world is breast cancer. Among the several low, moderate and high penetrance genes conferring susceptibility to breast cancer, PTEN is one which is known to be mutated in many tumor types. In this study, we predicted and analyzed the impact of three deleterious coding non-synonymous single nucleotide polymorphisms rs121909218 (G129E), rs121909229 (R130Q) and rs57374291 (D107N) in the PTEN gene on the phenotype of breast tumors using computational tools SIFT, Polyphen-2, PROVEAN, MUPro, POPMusic and the GETAREA server.
Keywords
Breast cancer; PTEN; nSNPs; genetic factors; computational tools;
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1 Adamczyk A, Niemiec J, Janecka A, et al (2015). Prognostic value of PIK3CA mutation status, PTEN and androgen receptor expression for metastasis-free survival in HER2-positive breast cancer patients treated with trastuzumab in adjuvant setting. Pol J Pathol, 66, 133-41.
2 Adzhubei IA, Schmidt S, Peshkin L, et al (2010). A method and server for predicting damaging missense mutations. Nat Methods, 7, 248-9.   DOI
3 Black JL, Harrell JC, Leisner TM, et al (2015). CIB1 depletion impairs cell survival and tumor growth in triple-negative breast cancer. Breast Cancer Res Treat, 152, 337-46.   DOI
4 Cariaso M, Lennon G (2012). SNPedia: a wiki supporting personal genome annotation, interpretation and analysis. Nucleic Acids Res, 40, 1308-12.   DOI
5 Caserta E, Egriboz O, Wang H, et al (2015). Noncatalytic PTEN missense mutation predisposes to organ-selective cancer development in vivo. Genes Dev, 29, 1707-20.   DOI
6 Cheng J, Randall A, Baldi P (2006). Prediction of protein stability changes for single-site mutations using support vector machines. Proteins, 62, 1125-32.
7 Choi Y, Sims GE, Murphy S, et al (2012). Predicting the functional effect of amino acid substitutions and indels. PLoS One, 7, 46688.   DOI
8 DeGraffenried LA, Fulcher L, Friedrichs WE, et al (2004). Reduced PTEN expression in breast cancer cells confers susceptibility to inhibitors of the PI3 kinase/Akt pathway. Ann Oncol, 15, 1510-6.   DOI
9 Dehouck Y, Grosfils A, Folch B, et al (2009). Fast and accurate predictions of protein stability changes upon mutations using statistical potentials and neural networks: PoPMuSiC-2.0. Bioinformatics, 25, 2537-43.   DOI
10 DeSantis C, Ma J, Bryan L, et al (2014). Breast cancer statistics, 2013. CA Cancer J Clin, 64, 52-62.   DOI
11 Fraczkiewicz R, Braun W (1998). Exact and Efficient Analytical Calculation of the Accessible Surface Areas and Their Gradients for Macromolecules. J Comp Chem, 19, 319-33.   DOI
12 Gonzalez-Angulo AM, Ferrer-Lozano J, Stemke-Hale K, et al (2011). PI3K pathway mutations and PTEN levels in primary and metastatic breast cancer. Mol Cancer Ther, 10, 1093-101.   DOI
13 Haiman CA, Stram DO, Cheng I, et al (2006). Common genetic variation at PTEN and risk of sporadic breast and prostate cancer. Cancer Epidemiol Biomarkers Prev, 15, 1021-5.   DOI
14 Heikkinen T, Greco D, Pelttari LM, et al (2011). Variants on the promoter region of PTEN affect breast cancer progression and patient survival. Breast Cancer Res, 13, 130.   DOI
15 International HapMap C, Altshuler DM, Gibbs RA, et al (2010). Integrating common and rare genetic variation in diverse human populations. Nature, 467, 52-8.   DOI
16 Kechagioglou P, Papi RM, Provatopoulou X, et al (2014). Tumor suppressor PTEN in breast cancer: heterozygosity, mutations and protein expression. Anticancer Res, 34, 1387-400.
17 Kumar P, Henikoff S, Ng PC (2009). Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc, 4, 1073-81.   DOI
18 Kwan ML, Kushi LH, Weltzien E, et al (2009). Epidemiology of breast cancer subtypes in two prospective cohort studies of breast cancer survivors. Breast Cancer Res, 11, 31.
19 Lindahl E, Azuara C, Koehl P, et al (2006). NOMAD-Ref: visualization, deformation and refinement of macromolecular structures based on all-atom normal mode analysis. Nucleic Acids Res, 34, 52-6.
20 Lee JO, Yang H, Georgescu MM, et al (1999). Crystal structure of the PTEN tumor suppressor: implications for its phosphoinositide phosphatase activity and membrane association. Cell, 99, 323-34.   DOI
21 Maggi LB, Jr., Weber JD (2015). Targeting PTEN-defined breast cancers with a one-two punch. Breast Cancer Res, 17, 51.   DOI
22 Martin AM, Weber BL (2000). Genetic and hormonal risk factors in breast cancer. J Natl Cancer Inst, 92, 1126-35.   DOI
23 Ng PC, Henikoff S (2003). SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res, 31, 3812-4.   DOI
24 Ng PC, Henikoff S (2006). Predicting the effects of amino acid substitutions on protein function. Annu Rev Genomics Hum Genet, 7, 61-80.   DOI
25 Saika K, Sobue T (2013). [Cancer statistics in the world]. Gan To Kagaku Ryoho, 40, 2475-80.
26 Schaefer C, Meier A, Rost B, et al (2012). SNPdbe: constructing an nsSNP functional impacts database. Bioinformatics, 28, 601-2.   DOI
27 Sherry ST, Ward MH, Kholodov M, et al (2001). dbSNP: the NCBI database of genetic variation. Nucleic Acids Res, 29, 308-11.   DOI
28 Sood A, Ghosh AK (2006). Literature search using PubMed: an essential tool for practicing evidence- based medicine. J Assoc Physicians India, 54, 303-8.