The challenge of determining the impact of FUT3 tumor-associated polymorphism rs2306969 (-6951 C> T) in invasive breast cancer cells

  • Jessica Catarine Frutuoso do Nascimento
  • Anderson de Oliveira Vasconcelos
  • Maria Aparecida Barreto Lopes Seabra
  • Eduardo Isidoro Carneiro Beltrão
  • Cíntia Renata Costa RochaEmail author
Short Communication


FUT3 gene is responsible for encode an homonymous α1,3/4-fucosyltransferase involved in the synthesis of sialyl-Lewis antigens. FUT3-fucosylated glycoconjugates play key roles in pathways involved in tumor biology and metastasis, such as cellular ligation to E-selectins, TGF-β-induced epithelial-mesenchymal transition, NK cell-mediated tumor cytotoxicity and apoptosis. Tumor-associated FUT3 promoter polymorphism rs2306969 (-6951 C> T, position related to the gene’s translation start site) has been linked to breast, ovarian and intestinal gastric cancer. Although non-coding polymorphisms accounts for the majority of variations founded in breast cancer, their functional roles are still poorly understood. This study aimed to investigate the impact of different alleles for this variation in FUT3 expression of invasive breast tumors. A luciferase reporter assay was performed using two breast tumor cell lines to evaluate respectively the impact of FUT3 rs2306969 (-6951 CC) and (-6951 TT) on protein expression. Gene and protein expressions were also measured in twenty-nine fresh biopsies of invasive breast tumors. Rs2306969 did not significantly influence FUT3 expression in both used systems. However, this study is defiant since the biological role of this polymorphism in breast cancer and other tumor types could be linked to cis/trans modulation of other genes, respond to different environmental stimuli or impact gene expression only in association with other variations. Rs2306969 did not modulate FUT3 expression in breast tumors under non-stimulated conditions. Nevertheless, our study contributes to the notably challenging task that is to understand how non-coding polymorphisms can drive the overall risk in cancer development.


α1,3/4-Fucosyltransferase Breast cancer Non-coding single nucleotide polymorphism SNP functional analysis 



We gratefully acknowledge Professor Dayane Gomes (Universidade Federal de Pernambuco) for the support during the execution of the Western blotting assays of this work and Professors João Ricardo Mendes and Sergio Crovella (Universidade Federal de Pernambuco) for provided us the Lipofectamine 3000 reagent and the pGL4 vectors, respectively. This work was supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in this study involving human participants were in accordance with international ethical standards and were approved by the Comité de Ética em Pesquisa Envolvendo Seres Humanos do Centro de Ciências da Saúde da Universidade Federal de Pernambuco (CAAE 06586612.9.0000.5208—No. 140.876).

Informed consent

Written informed consent was obtained from all participants included in this study.


  1. 1.
    Khurana E, Fu Y, Chakravarty D, Demichelis F, Rubin MA, Gerstein M (2016) Role of non-coding sequence variants in cancer. Nat Rev Genet 17(2):93–108. CrossRefGoogle Scholar
  2. 2.
    Nascimento JCF, Ferreira SA, Vasconcelos JL, Barbosa BT, Bezerra MF, Rocha CR, Beltrão EI (2015) FUT3 role in breast invasive ductal carcinoma: investigating its gene promoter and protein expression. Exp Mol Pathol 99(3):409–415. CrossRefGoogle Scholar
  3. 3.
    Sellers TA, Huang Y, Cunningham J et al (2008) Association of single nucleotide polymorphisms in glycosylation genes with risk of epithelial ovarian cancer. Cancer Epidemiol Biomark Prev 17(2):397–404. CrossRefGoogle Scholar
  4. 4.
    Duell EJ, Bonet C, Muñoz X et al (2015) Variation at ABO histo-blood group and FUT loci and diffuse and intestinal gastric cancer risk in a European population. Int J Cancer 136(4):880–893. CrossRefGoogle Scholar
  5. 5.
    He M, Wu C, Xu J et al (2014) A genome wide association study of genetic loci that influence tumour biomarkers cancer antigen 19-9, carcinoembryonic antigen and alpha fetoprotein and their associations with cancer risk. Gut 63(1):143–151. CrossRefGoogle Scholar
  6. 6.
    Shokeen Y, Sharma NR, Vats A, Taneja V, Minhas S, Jauhri M, Sankaran S, Aggarwal S (2018) Identification of prognostic and susceptibility markers in chronic myeloid leukemia using next generation sequencing. Ethiop J Health Sci 28(2):135–146. CrossRefGoogle Scholar
  7. 7.
    Xie B, Li Y, Zhao R, Xu Y, Wu Y, Wang J, Xia D, Han W, Chen D (2018) Identification of key genes and miRNAs in osteosarcoma patients with chemoresistance by bioinformatics analysis. Biomed Res Int 2018:1–10. Google Scholar
  8. 8.
    Burdick MM, Henson KA, Delgadillo LF, Choi YE, Goetz DJ, Tees DFJ, Benencia F (2012) Expression of E-selectin ligands on circulating tumor cells: Cross-regulation with cancer stem cell regulatory pathways? Front Oncol 2:103. CrossRefGoogle Scholar
  9. 9.
    Padró M, Cobler L, Garrido M, Bolós C (1810) Down-regulation of FUT3 and FUT5 by shRNA alters Lewis antigens expression and reduces the adhesion capacities of gastric cancer cells. Biochim Biophys Acta 12:1141–1149. Google Scholar
  10. 10.
    Zhan L, Chen L, Chen Z (2018) Knockdown of FUT3 disrupts the proliferation, migration, tumorigenesis and TGF-β induced EMT in pancreatic cancer cells. Oncol Lett 16(1):924–930. Google Scholar
  11. 11.
    Higai K, Ichikawa A, Matsumoto K (2006) Binding of sialyl Lewis X antigen to lectin like receptors on NK cells induces cytotoxicity and tyrosine phosphorylation of a 17-kDa protein. Biochim Biophys Acta 1760(9):1355–1363. CrossRefGoogle Scholar
  12. 12.
    Zhang B, Van Roosmalen IAM, Reis CR, Setroikromo R, Quax WJ (2018) Death receptor 5 is activated by fucosylation in colon cancer cells. FEBS J 286:555–571. CrossRefGoogle Scholar
  13. 13.
    Breiman A, Robles MDL, Trécesson SC, Echasserieau K, Bernardeau K, Drickamer K, Imberty A, Barillé-Nion S, Altare F, LePendu J (2016) Carcinoma-associated fucosylated antigens are markers of the epithelial state and can contribute to cell adhesion through CLEC17A (Prolectin). Oncotarget 7(12):14064–14082. CrossRefGoogle Scholar
  14. 14.
    Carrascal MA, Silva M, Ramalho JS et al (2018) Inhibition of fucosylation in human invasive ductal carcinoma reduces E-selectin ligand expression, cell proliferation and ERK1/2 and p38 MAPK activation. Mol Oncol 12(5):579–593. CrossRefGoogle Scholar
  15. 15.
    Julien S, Ivetic A, Grigoriadis A et al (2011) Selectin ligand sialyl-Lewis × antigen drives metastasis of hormone-dependent breast cancers. Cancer Res 71(24):7683–7693. CrossRefGoogle Scholar
  16. 16.
    Liu LL, Zhao H, Ma TF, Ge F, Chen C, Zhang Y (2015) Identification of valid reference genes for the normalization of RT-qPCR expression studies in human breast cancer cell lines treated with and without transient transfection. PLoS ONE 10(1):e0117058. CrossRefGoogle Scholar
  17. 17.
    Nica AC, Dermitzakis ET (2013) Expression quantitative trait loci: present and future. Philos Trans R Soc Lond B Biol Sci 368(1620):20120362. CrossRefGoogle Scholar
  18. 18.
    Nordén R, Samuelsson E, Nyström K (2017) NFκB-mediated activation of the cellular FUT3, 5 and 6 gene cluster by herpes simplex virus type 1. Glycobiology 27(11):999–1005. CrossRefGoogle Scholar
  19. 19.
    Lauc G, Essafi A, Huffman JE et al (2010) Genomics meets glycomics-the first GWAS study of human N-glycome identifies HNF1α as a master regulator of plasma protein fucosylation. PLoS Genet 6(12):e1001256. CrossRefGoogle Scholar
  20. 20.
    Ramsuran V, Ewy R, Nguyen H, Kulkarni S (2018) Variation in the untranslated genome and susceptibility to infections. Front Immunol 9:2046. CrossRefGoogle Scholar
  21. 21.
    Guo YZ, Sun HH, Wang XT, Wang MT (2018) Transcriptomic analysis reveals key lncRNAs associated with ribosomal biogenesis and epidermis differentiation in head and neck squamous cell carcinoma. J Zhejiang Univ Sci B 19(9):674–688. CrossRefGoogle Scholar
  22. 22.
    Barretina J, Caponigro G, Stransky N et al (2012) The cancer cell line encyclopedia enables predictive modeling of anticancer drug sensitivity. Nature 483(7391):603–607. CrossRefGoogle Scholar
  23. 23.
    Ramamoorthy S, Cidlowski JA (2016) Corticosteroids-mechanisms of action in health and disease. Rheum Dis Clin North Am 42(1):15–31. CrossRefGoogle Scholar
  24. 24.
    Fagny M, Paulson JN, Kuijjer ML, Sonawanec AR, Chena C, Lopes-Ramosa CM, Glassc K, Quackenbusha J, Platiga J (2017) Exploring regulation in tissues with eQTL networks. Proc Natl Acad Sci USA 114(37):E7841–E7850. CrossRefGoogle Scholar
  25. 25.
    Murk W, DeWan AT (2016) Exhaustive genome-wide search for SNP-SNP interactions across 10 human diseases. G3 (Bethesda) 6(7):2043–2050. CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  • Jessica Catarine Frutuoso do Nascimento
    • 1
  • Anderson de Oliveira Vasconcelos
    • 2
  • Maria Aparecida Barreto Lopes Seabra
    • 1
  • Eduardo Isidoro Carneiro Beltrão
    • 1
    • 2
  • Cíntia Renata Costa Rocha
    • 1
    • 2
    Email author
  1. 1.Laboratório de Imunopatologia Keizo AsamiUniversidade Federal de PernambucoRecifeBrazil
  2. 2.Departamento de BioquímicaUniversidade Federal de PernambucoRecifeBrazil

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