Targeting Breast Cancer with N-Acetyl-D-Glucosamine: Integrating Machine Learning and Cellular Assays for Promising Results

  • Authors: Baysal Ö.1, Genç D.2, Silme R.S.3, Kırboğa K.K.4, Çoban D.5, Ghafoor N.A.6, Tekin L.7, Bulut O.8
  • Affiliations:
    1. Department of Molecular Biology and Genetics, Faculty of Science, Molecular Microbiology Unit, Muğla Sıtkı Koçman Üniversitesi
    2. Faculty of Health Sciences,, Muğla Sıtkı Koçman University,
    3. Center for Research and Practice in Biotechnology and Genetic Engineering,, Istanbul University
    4. Department of Bioengineering,, Bilecik Seyh Edebali University
    5. Department of Molecular Biology and Genetics, Faculty of Science, Molecular Microbiology Unit,, Muğla Sıtkı Koçman University,
    6. Department of Molecular Biology and Genetics, Faculty of Science,, Muğla Sıtkı Koçman University
    7. Department of Pathology, Faculty of Medicine,, Muğla Sıtkı Koçman University,
    8. Milas Faculty of Veterinary Medicine,, Muğla Sıtkı Koçman Üniversitesi
  • Issue: Vol 24, No 5 (2024)
  • Pages: 334-347
  • Section: Oncology
  • URL: https://rjsocmed.com/1871-5206/article/view/644174
  • DOI: https://doi.org/10.2174/0118715206270568231129054853
  • ID: 644174

Cite item

Full Text

Abstract

Background:Breast cancer is a common cancer with high mortality rates. Early diagnosis is crucial for reducing the prognosis and mortality rates. Therefore, the development of alternative treatment options is necessary.

Objective:This study aimed to investigate the inhibitory effect of N-acetyl-D-glucosamine (D-GlcNAc) on breast cancer using a machine learning method. The findings were further confirmed through assays on breast cancer cell lines.

Methods:MCF-7 and 4T1 cell lines (ATCC) were cultured in the presence and absence of varying concentrations of D-GlcNAc (0.5 mM, 1 mM, 2 mM, and 4 mM) for 72 hours. A xenograft mouse model for breast cancer was established by injecting 4T1 cells into mammary glands. D-GlcNAc (2 mM) was administered intraperitoneally to mice daily for 28 days, and histopathological effects were evaluated at pre-tumoral and post-tumoral stages.

Results:Treatment with 2 mM and 4 mM D-GlcNAc significantly decreased cell proliferation rates in MCF-7 and 4T1 cell lines and increased Fas expression. The number of apoptotic cells was significantly higher than untreated cell cultures (p < 0.01 - p < 0.0001). D-GlcNAc administration also considerably reduced tumour size, mitosis, and angiogenesis in the post-treatment group compared to the control breast cancer group (p < 0.01 - p < 0.0001). Additionally, molecular docking/dynamic analysis revealed a high binding affinity of D-GlcNAc to the marker protein HER2, which is involved in tumour progression and cell signalling.

Conclusion:Our study demonstrated the positive effect of D-GlcNAc administration on breast cancer cells, leading to increased apoptosis and Fas expression in the malignant phenotype. The binding affinity of D-GlcNAc to HER2 suggests a potential mechanism of action. These findings contribute to understanding D-GlcNAc as a potential anti-tumour agent for breast cancer treatment.

About the authors

Ömür Baysal

Department of Molecular Biology and Genetics, Faculty of Science, Molecular Microbiology Unit, Muğla Sıtkı Koçman Üniversitesi

Author for correspondence.
Email: info@benthamscience.net

Deniz Genç

Faculty of Health Sciences,, Muğla Sıtkı Koçman University,

Email: info@benthamscience.net

Ragıp Soner Silme

Center for Research and Practice in Biotechnology and Genetic Engineering,, Istanbul University

Email: info@benthamscience.net

Kevser Kübra Kırboğa

Department of Bioengineering,, Bilecik Seyh Edebali University

Email: info@benthamscience.net

Dilek Çoban

Department of Molecular Biology and Genetics, Faculty of Science, Molecular Microbiology Unit,, Muğla Sıtkı Koçman University,

Email: info@benthamscience.net

Naeem Abdul Ghafoor

Department of Molecular Biology and Genetics, Faculty of Science,, Muğla Sıtkı Koçman University

Email: info@benthamscience.net

Leyla Tekin

Department of Pathology, Faculty of Medicine,, Muğla Sıtkı Koçman University,

Email: info@benthamscience.net

Osman Bulut

Milas Faculty of Veterinary Medicine,, Muğla Sıtkı Koçman Üniversitesi

Email: info@benthamscience.net

References

  1. Lovrics, O.; Butt, J.; Lee, Y.; Lovrics, P.; Boudreau, V.; Anvari, M.; Hong, D.; Doumouras, A.G. The effect of bariatric surgery on breast cancer incidence and characteristics: A meta-analysis and systematic review. Am. J. Surg., 2021, 222(4), 715-722. doi: 10.1016/j.amjsurg.2021.03.016 PMID: 33771341
  2. Maughan, K.L.; Lutterbie, M.A.; Ham, P.S. Treatment of breast cancer. Am. Fam. Physician, 2010, 81(11), 1339-1346. PMID: 20521754
  3. Bhushan, A.; Gonsalves, A.; Menon, J.U. Current state of breast cancer diagnosis, treatment, and theranostics. Pharmaceutics, 2021, 13(5), 723. doi: 10.3390/pharmaceutics13050723 PMID: 34069059
  4. Liang, Y.; Xu, W.; Liu, S.; Chi, J.; Zhang, J.; Sui, A.; Wang, L.; Liang, Z.; Li, D.; Chen, Y.; Niu, H. N-acetyl-glucosamine sensitizes non-small cell lung cancer cells to trail-induced apoptosis by activating death receptor 5. Cell. Physiol. Biochem., 2018, 45(5), 2054-2070. doi: 10.1159/000488042 PMID: 29533936
  5. Mattaveewong, T.; Wongkrasant, P.; Chanchai, S.; Pichyangkura, R.; Chatsudthipong, V.; Muanprasat, C. Chitosan oligosaccharide suppresses tumor progression in a mouse model of colitis-associated colorectal cancer through AMPK activation and suppression of NF-κB and mTOR signaling. Carbohydr. Polym., 2016, 145, 30-36. doi: 10.1016/j.carbpol.2016.02.077 PMID: 27106148
  6. Medina, S.H.; Tekumalla, V.; Chevliakov, M.V.; Shewach, D.S.; Ensminger, W.D.; El-Sayed, M.E.H. N-acetylgalactosamine-functionalized dendrimers as hepatic cancer cell-targeted carriers. Biomaterials, 2011, 32(17), 4118-4129. doi: 10.1016/j.biomaterials.2010.11.068 PMID: 21429574
  7. Stowell, S.R.; Ju, T.; Cummings, R.D. Protein glycosylation in cancer. Annu. Rev. Pathol., 2015, 10(1), 473-510. doi: 10.1146/annurev-pathol-012414-040438 PMID: 25621663
  8. Varki, A.; Kannagi, R.; Toole, B.; Stanley, P. Glycosylation changes in cancer. In: Essentials of Glycobiology, 3rd ed.; Varki, A.; Cummings, R.D.; Esko, J.D.; Stanley, P.; Hart, G.W.; Aebi, M.; Darvill, A.G.; Kinoshita, T.; Packer, N.H.; Prestegard, J.H., Eds.; Cold Spring Harbor Laboratory Press: Cold Spring Harbor (NY), 2015; pp. 597-609.
  9. Chou, T.Y.; Hart, G.W. O-linked N-acetylglucosamine and cancer: Messages from the glycosylation of c-Myc. Adv. Exp. Med. Biol., 2001, 491, 413-418. doi: 10.1007/978-1-4615-1267-7_26 PMID: 14533811
  10. Baysal, Ö.; Silme, R.; Karaaslan, C. Genetic uniformity of a specific region in SARS-CoV-2 genome and in-silico target-oriented repurposing of N-Acetyl-D-Glucosamine Preprints, 2020, 2020050397. doi: 10.20944/preprints202005.0397.v1
  11. Baysal, Ö.; Silme, R.; Karaaslan, C.; Ignatov, A. Genetic uniformity of a specific region in SARS-CoV-2 genome and repurposing of N-acetyl-D-glucosamine. Fresenius Environ. Bull., 2021, 30, 2848-2857.
  12. Baysal, Ö.; Abdul Ghafoor, N.; Silme, R.S.; Ignatov, A.N.; Kniazeva, V. Molecular dynamics analysis of N-acetyl-D-glucosamine against specific SARS-CoV-2’s pathogenicity factors. PLoS One, 2021, 16(5), e0252571. doi: 10.1371/journal.pone.0252571 PMID: 34043733
  13. Schultz, M.J.; Swindall, A.F.; Bellis, S.L. Regulation of the metastatic cell phenotype by sialylated glycans. Cancer Metastasis Rev., 2012, 31(3-4), 501-518. doi: 10.1007/s10555-012-9359-7 PMID: 22699311
  14. Buehring, G.C.; Shen, H.M.; Jensen, H.M.; Jin, D.L.; Hudes, M.; Block, G. Exposure to bovine leukemia virus is associated with breast cancer: A case-control study. PLoS One, 2015, 10(9), e0134304. doi: 10.1371/journal.pone.0134304 PMID: 26332838
  15. Melana, S.M.; Nepomnaschy, I.; Hasa, J.; Djougarian, A.; Djougarian, A.; Holland, J.F.; Pogo, B.G.T. Detection of human mammary tumor virus proteins in human breast cancer cells. J. Virol. Methods, 2010, 163(1), 157-161. doi: 10.1016/j.jviromet.2009.09.015 PMID: 19781575
  16. Tsai, J.H.; Hsu, C.S.; Tsai, C.H.; Su, J.M.; Liu, Y.T.; Cheng, M.H.; Wei, J.C.C.; Chen, F.L.; Yang, C.C. Relationship between viral factors, axillary lymph node status and survival in breast cancer. J. Cancer Res. Clin. Oncol., 2006, 133(1), 13-21. doi: 10.1007/s00432-006-0141-5 PMID: 16865407
  17. Joshi, D.; Quadri, M.; Gangane, N.; Joshi, R.; Gangane, N. Association of Epstein Barr virus infection (EBV) with breast cancer in rural Indian women. PLoS One, 2009, 4(12), e8180. doi: 10.1371/journal.pone.0008180 PMID: 19997605
  18. Fawzy, S.; Sallam, M.; Mohammad, A. N. Detection of Epstein–Barr virus in breast carcinoma in Egyptian women. Clin. Biochem., 2008, 41(7-8), 486-492. doi: 10.1016/j.clinbiochem.2007.12.017 PMID: 18258188
  19. Hachana, M.; Amara, K.; Ziadi, S.; Romdhane, E.; Gacem, R.B.; Trimeche, M. Investigation of Epstein–Barr virus in breast carcinomas in Tunisia. Pathol. Res. Pract., 2011, 207(11), 695-700. doi: 10.1016/j.prp.2011.09.007 PMID: 22024152
  20. Harkins, L.E.; Matlaf, L.A.; Soroceanu, L.; Klemm, K.; Britt, W.J.; Wang, W.; Bland, K.I.; Cobbs, C.S. Detection of human cytomegalovirus in normal and neoplastic breast epithelium. Herpesviridae, 2010, 1(1), 8. doi: 10.1186/2042-4280-1-8 PMID: 21429243
  21. Taher, C.; de Boniface, J.; Mohammad, A.A.; Religa, P.; Hartman, J.; Yaiw, K.C.; Frisell, J.; Rahbar, A.; Söderberg-Naucler, C. High prevalence of human cytomegalovirus proteins and nucleic acids in primary breast cancer and metastatic sentinel lymph nodes. PLoS One, 2013, 8(2), e56795. doi: 10.1371/journal.pone.0056795 PMID: 23451089
  22. Costa, H.; Touma, J.; Davoudi, B.; Benard, M.; Sauer, T.; Geisler, J.; Vetvik, K.; Rahbar, A.; Söderberg-Naucler, C. Human cytomegalovirus infection is correlated with enhanced cyclooxygenase-2 and 5-lipoxygenase protein expression in breast cancer. J. Cancer Res. Clin. Oncol., 2019, 145(8), 2083-2095. doi: 10.1007/s00432-019-02946-8 PMID: 31203442
  23. Yang, Z.; Tang, X.; Meng, G.; Benesch, M.; Mackova, M.; Belon, A.; Serrano-Lomelin, J.; Goping, I.; Brindley, D.; Hemmings, D. Latent cytomegalovirus infection in female mice increases breast cancer metastasis. Cancers, 2019, 11(4), 447. doi: 10.3390/cancers11040447 PMID: 30934926
  24. Alibek, K.; Kakpenova, A.; Mussabekova, A.; Sypabekova, M.; Karatayeva, N. Role of viruses in the development of breast cancer. Infect. Agent. Cancer, 2013, 8(1), 32. doi: 10.1186/1750-9378-8-32 PMID: 24138789
  25. Richardson, A. Is breast cancer caused by late exposure to a common virus? Med. Hypotheses, 1997, 48(6), 491-497. doi: 10.1016/S0306-9877(97)90118-3 PMID: 9247892
  26. Shamshirian, A.; Aref, A.R.; Yip, G.W.; Ebrahimi, W.M.; Heydari, K.; Razavi, B.S.; Hamzehgardeshi, Z.; Shamshirian, D.; Moosazadeh, M.; Alizadeh-Navaei, R. Diagnostic value of serum HER2 levels in breast cancer: A systematic review and meta-analysis. BMC Cancer, 2020, 20(1), 1049. doi: 10.1186/s12885-020-07545-2 PMID: 33129287
  27. Pro, O. HER2 in Breast Cancer: ESMO biomarker factsheet. 2015. Available from: https://oncologypro.esmo.org/education-library/factsheets-on-biomarkers/her2-in-breast-cancer (Accessed on: 2023).
  28. Borgquist, S.; Zhou, W.; Jirström, K.; Amini, R.M.; Sollie, T.; Sørlie, T.; Blomqvist, C.; Butt, S.; Wärnberg, F. The prognostic role of HER2 expression in ductal breast carcinoma in situ (DCIS); a population-based cohort study. BMC Cancer, 2015, 15(1), 468. doi: 10.1186/s12885-015-1479-3 PMID: 26062614
  29. Ignatov, T.; Eggemann, H.; Burger, E.; Fettke, F.; Costa, S.D.; Ignatov, A. Moderate level of HER2 expression and its prognostic significance in breast cancer with intermediate grade. Breast Cancer Res. Treat., 2015, 151(2), 357-364. doi: 10.1007/s10549-015-3407-2 PMID: 25926338
  30. Chiu, M.; Taurino, G.; Bianchi, M.G.; Kilberg, M.S.; Bussolati, O. Asparagine synthetase in cancer: Beyond acute lymphoblastic leukemia. Front. Oncol., 2020, 9, 1480. doi: 10.3389/fonc.2019.01480 PMID: 31998641
  31. Shen, X.; Jain, A.; Aladelokun, O.; Yan, H.; Gilbride, A.; Ferrucci, L.M.; Lu, L.; Khan, S.A.; Johnson, C.H. Asparagine, colorectal cancer, and the role of sex, genes, microbes, and diet: A narrative review. Front. Mol. Biosci., 2022, 9, 958666. doi: 10.3389/fmolb.2022.958666 PMID: 36090030
  32. Davidsen, K.; Sullivan, L.B. Free asparagine or die: Cancer cells require proteasomal protein breakdown to survive asparagine depletion. Cancer Discov., 2020, 10(11), 1632-1634. doi: 10.1158/2159-8290.CD-20-1251
  33. Gaulton, A.; Hersey, A.; Nowotka, M.; Bento, A.P.; Chambers, J.; Mendez, D.; Mutowo, P.; Atkinson, F.; Bellis, L.J.; Cibrián-Uhalte, E. The ChEMBL database in 2017. Nucleic Acids Res., 2017, 45(D1), D945-D954. doi: 10.1093/nar/gkw1074
  34. Jaeger, S.; Fulle, S.; Turk, S. Mol2vec: Unsupervised machine learning approach with chemical intuition. J. Chem. Inf. Model., 2018, 58(1), 27-35. doi: 10.1021/acs.jcim.7b00616 PMID: 29268609
  35. Huang, K.; Fu, T.; Glass, L.M.; Zitnik, M.; Xiao, C.; Sun, J. DeepPurpose: A deep learning library for drug–target interaction prediction. Bioinformatics, 2021, 36(22-23), 5545-5547. doi: 10.1093/bioinformatics/btaa1005 PMID: 33275143
  36. RDKit: Open-source cheminformatics 2022. Available from: https://www.rdkit.org/
  37. Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V. Scikit-learn: Machine learning in python. J. Mach. Learn. Res., 2011, 12, 2825-2830.
  38. Pedregosa, F.; Varoquaux, G.e.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M. Scikit-learn: Machine Learning in Python; Dataquest, 2018.
  39. Cucina, A.; Proietti, S.; D’Anselmi, F.; Coluccia, P.; Dinicola, S.; Frati, L.; Bizzarri, M. Evidence for a biphasic apoptotic pathway induced by melatonin in MCF-7 breast cancer cells. J. Pineal Res., 2009, 46(2), 172-180. doi: 10.1111/j.1600-079X.2008.00645.x PMID: 19175854
  40. Matera, G.; Lupi, M.; Ubezio, P. Heterogeneous cell response to topotecan in a CFSE-based proliferation test. Cytometry A, 2004, 62A(2), 118-128. doi: 10.1002/cyto.a.20097 PMID: 15536634
  41. Pulaski, B. A.; Ostrand-Rosenberg, S. Mouse 4T1 breast tumor model. Curr. Protocols Immunol., 2000, 39(1), 20.22.21-20.22.16. doi: 10.1002/0471142735.im2002s39
  42. Zheng, L.; Zhou, B.; Meng, X.; Zhu, W.; Zuo, A.; Wang, X.; Jiang, R.; Yu, S. A model of spontaneous mouse mammary tumor for human estrogen receptor- and progesterone receptor-negative breast cancer. Int. J. Oncol., 2014, 45(6), 2241-2249. doi: 10.3892/ijo.2014.2657 PMID: 25230850
  43. Morris, G.M.; Huey, R.; Lindstrom, W.; Sanner, M.F.; Belew, R.K.; Goodsell, D.S.; Olson, A.J. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem., 2009, 30(16), 2785-2791. doi: 10.1002/jcc.21256 PMID: 19399780
  44. Trott, O.; Olson, A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem., 2010, 31(2), 455-461. doi: 10.1002/jcc.21334 PMID: 19499576
  45. Grosdidier, A.; Zoete, V.; Michielin, O. SwissDock, a proteinsmall molecule docking web service based on EADock DSS. Nucleic Acids Res, 2011, 39(Web Server issue), W270-277. doi: 10.1093/nar/gkr366
  46. Phillips, J.C.; Hardy, D.J.; Maia, J.D.C.; Stone, J.E.; Ribeiro, J.V.; Bernardi, R.C.; Buch, R.; Fiorin, G.; Hénin, J.; Jiang, W.; McGreevy, R.; Melo, M.C.R.; Radak, B.K.; Skeel, R.D.; Singharoy, A.; Wang, Y.; Roux, B.; Aksimentiev, A.; Luthey-Schulten, Z.; Kalé, L.V.; Schulten, K.; Chipot, C.; Tajkhorshid, E. Scalable molecular dynamics on CPU and GPU architectures with NAMD. J. Chem. Phys., 2020, 153(4), 044130. doi: 10.1063/5.0014475 PMID: 32752662
  47. Huang, J.; Rauscher, S.; Nawrocki, G.; Ran, T.; Feig, M.; de Groot, B.L.; Grubmüller, H.; MacKerell, A.D., Jr CHARMM36m: An improved force field for folded and intrinsically disordered proteins. Nat. Methods, 2017, 14(1), 71-73. doi: 10.1038/nmeth.4067 PMID: 27819658
  48. Lee, J.; Cheng, X.; Swails, J.M.; Yeom, M.S.; Eastman, P.K.; Lemkul, J.A.; Wei, S.; Buckner, J.; Jeong, J.C.; Qi, Y.; Jo, S.; Pande, V.S.; Case, D.A.; Brooks, C.L., III; MacKerell, A.D., Jr; Klauda, J.B. Im, W. CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J. Chem. Theory Comput., 2016, 12(1), 405-413. doi: 10.1021/acs.jctc.5b00935 PMID: 26631602
  49. Comşa, Ş.; Cîmpean, A.M.; Raica, M. The story of MCF-7 breast cancer cell line: 40 years of experience in research. Anticancer Res., 2015, 35(6), 3147-3154. PMID: 26026074
  50. Lee, A. V.; Oesterreich, S.; Davidson, N. E. MCF-7 cells—changing the course of breast cancer research and care for 45 years. JNCI: J. Nation. Cancer Institute, 2015, 107(7), djv073. doi: 10.1093/jnci/djv073
  51. Ma, J.; Hart, G.W. O-GlcNAc profiling: From proteins to proteomes. Clin. Proteomics, 2014, 11(1), 8. doi: 10.1186/1559-0275-11-8 PMID: 24593906
  52. Elola, M.; Fernandez, M.; Ferragut, F.; Vm, C.; Bracalente, C.; Bravo, I.; Cagnoni, A.; Nuñez, M.; Morosi, L.; Quintá, H. Glycosylation-dependent binding of galectin-8 to activated leukocyte cell adhesion molecule (ALCAM/CD166) promotes its surface segregation on breast cancer cells. Biochim. Biophys. Acta, 2016, 1860(10), 2255-2268. doi: 10.1016/j.bbagen.2016.04.019
  53. Peixoto, A.; Relvas-Santos, M.; Azevedo, R.; Santos, L.L.; Ferreira, J.A. Protein glycosylation and tumor microenvironment alterations driving cancer hallmarks. Front. Oncol., 2019, 9, 380. doi: 10.3389/fonc.2019.00380 PMID: 31157165
  54. Kumar, P.; Tambe, P.; Paknikar, K.M.; Gajbhiye, V. Folate/N -acetyl glucosamine conjugated mesoporous silica nanoparticles for targeting breast cancer cells: A comparative study. Colloids Surf. B Biointerfaces, 2017, 156, 203-212. doi: 10.1016/j.colsurfb.2017.05.032 PMID: 28531877
  55. Cheng, L.; Cao, L.; Wu, Y.; Xie, W.; Li, J.; Guan, F.; Tan, Z. Bisecting N-Acetylglucosamine on EGFR inhibits malignant phenotype of breast cancer via down-regulation of EGFR/Erk signaling. Front. Oncol., 2020, 10, 929. doi: 10.3389/fonc.2020.00929
  56. Mereiter, S.; Balmaña, M.; Campos, D.; Gomes, J.; Reis, C.A. Glycosylation in the era of cancer-targeted therapy: Where are we heading? Cancer Cell, 2019, 36(1), 6-16. doi: 10.1016/j.ccell.2019.06.006 PMID: 31287993
  57. Xu, W.; Jiang, C.; Kong, X.; Liang, Y.; Rong, M.; Liu, W. Chitooligosaccharides and N-acetyl-D-glucosamine stimulate peripheral blood mononuclear cell-mediated antitumor immune responses. Mol. Med. Rep., 2012, 6(2), 385-390. doi: 10.3892/mmr.2012.918 PMID: 22614871
  58. Quastel, J.H.; Cantero, A. Inhibition of tumour growth by D-glucosamine. Nature, 1953, 171(4345), 252-254. doi: 10.1038/171252a0 PMID: 13036842
  59. Brasky, T.M.; Lampe, J.W.; Slatore, C.G.; White, E. Use of glucosamine and chondroitin and lung cancer risk in the VITamins And Lifestyle (VITAL) cohort. Cancer Causes Control, 2011, 22(9), 1333-1342. doi: 10.1007/s10552-011-9806-8 PMID: 21706174
  60. Kantor, E.D.; Lampe, J.W.; Peters, U.; Shen, D.D.; Vaughan, T.L.; White, E. Use of glucosamine and chondroitin supplements and risk of colorectal cancer. Cancer Causes Control, 2013, 24(6), 1137-1146. doi: 10.1007/s10552-013-0192-2 PMID: 23529472
  61. Kim, M.J.; Choi, M.Y.; Lee, D.H.; Roh, G.S.; Kim, H.J.; Kang, S.S.; Cho, G.J.; Kim, Y.S.; Choi, W.S. O-linked N-acetylglucosamine transferase enhances secretory clusterin expression via liver X receptors and sterol response element binding protein regulation in cervical cancer. Oncotarget, 2018, 9(4), 4625-4636. doi: 10.18632/oncotarget.23588 PMID: 29435130
  62. Taniguchi, N.; Kizuka, Y. Glycans and cancer. Adv. Cancer Res., 2015, 126, 11-51. doi: 10.1016/bs.acr.2014.11.001 PMID: 25727145
  63. Rivlin, M.; Navon, G. Glucosamine and N-acetyl glucosamine as new CEST MRI agents for molecular imaging of tumors. Sci. Rep., 2016, 6(1), 32648. doi: 10.1038/srep32648 PMID: 27600054
  64. Ghosh, S. Sialic acids and sialoglycans in endocrinal disorders. In: Sialic Acids and Sialoglycoconjugates in the Biology of Life, Health and Disease; Ghosh, S., Ed.; Academic Press, 2020; pp. 247-268. doi: 10.1016/B978-0-12-816126-5.00009-3
  65. Wu, S.; Zhang, Q.; Zhang, F.; Meng, F.; Liu, S.; Zhou, R.; Wu, Q.; Li, X.; Shen, L.; Huang, J.; Qin, J.; Ouyang, S.; Xia, Z.; Song, H.; Feng, X.H.; Zou, J.; Xu, P. HER2 recruits AKT1 to disrupt STING signalling and suppress antiviral defence and antitumour immunity. Nat. Cell Biol., 2019, 21(8), 1027-1040. doi: 10.1038/s41556-019-0352-z PMID: 31332347
  66. Läubli, H.; Varki, A. Sialic acid–binding immunoglobulin-like lectins (Siglecs) detect self-associated molecular patterns to regulate immune responses. Cell. Mol. Life Sci., 2020, 77(4), 593-605. doi: 10.1007/s00018-019-03288-x PMID: 31485715
  67. Crocker, P.R.; Paulson, J.C.; Varki, A. Siglecs and their roles in the immune system. Nat. Rev. Immunol., 2007, 7(4), 255-266. doi: 10.1038/nri2056 PMID: 17380156
  68. Macauley, M.S.; Crocker, P.R.; Paulson, J.C. Siglec-mediated regulation of immune cell function in disease. Nat. Rev. Immunol., 2014, 14(10), 653-666. doi: 10.1038/nri3737 PMID: 25234143
  69. van de Wall, S.; Santegoets, K.C.M.; van Houtum, E.J.H.; Büll, C.; Adema, G.J. Sialoglycans and siglecs can shape the tumor immune microenvironment. Trends Immunol., 2020, 41(4), 274-285. doi: 10.1016/j.it.2020.02.001 PMID: 32139317
  70. Hudak, J.E.; Canham, S.M.; Bertozzi, C.R. Glycocalyx engineering reveals a Siglec-based mechanism for NK cell immunoevasion. Nat. Chem. Biol., 2014, 10(1), 69-75. doi: 10.1038/nchembio.1388 PMID: 24292068
  71. Daly, J.; Carlsten, M.; O’Dwyer, M. Sugar Free: Novel immunotherapeutic approaches targeting siglecs and sialic acids to enhance natural killer cell cytotoxicity against cancer. Front. Immunol., 2019, 10, 1047. doi: 10.3389/fimmu.2019.01047 PMID: 31143186
  72. Bärenwaldt, A.; Läubli, H. The sialoglycan-Siglec glyco-immune checkpoint-a target for improving innate and adaptive anti-cancer immunity. Expert Opin. Ther. Targets, 2019, 23(10), 839-853. doi: 10.1080/14728222.2019.1667977 PMID: 31524529
  73. Beatson, R.; Tajadura-Ortega, V.; Achkova, D.; Picco, G.; Tsourouktsoglou, T.D.; Klausing, S.; Hillier, M.; Maher, J.; Noll, T.; Crocker, P.R.; Taylor-Papadimitriou, J.; Burchell, J.M. The mucin MUC1 modulates the tumor immunological microenvironment through engagement of the lectin Siglec-9. Nat. Immunol., 2016, 17(11), 1273-1281. doi: 10.1038/ni.3552 PMID: 27595232
  74. Rillahan, C.D.; Antonopoulos, A.; Lefort, C.T.; Sonon, R.; Azadi, P.; Ley, K.; Dell, A.; Haslam, S.M.; Paulson, J.C. Global metabolic inhibitors of sialyl- and fucosyltransferases remodel the glycome. Nat. Chem. Biol., 2012, 8(7), 661-668. doi: 10.1038/nchembio.999 PMID: 22683610
  75. Horstkorte, R.; Fuss, B. Cell adhesion molecules. In: Basic Neurochemistry, 8th ed; Brady, S.T.; Siegel, G.J.; Albers, R.W.; Price, D.L., Eds.; Academic Press, 2012; pp. 165-179. doi: 10.1016/B978-0-12-374947-5.00009-2
  76. Hart, G.W. Nutrient regulation of signaling and transcription. J. Biol. Chem., 2019, 294(7), 2211-2231. doi: 10.1074/jbc.AW119.003226 PMID: 30626734
  77. Chugh, S.; Gnanapragassam, V.S.; Jain, M.; Rachagani, S.; Ponnusamy, M.P.; Batra, S.K. Pathobiological implications of mucin glycans in cancer: Sweet poison and novel targets. Biochim. Biophys. Acta Rev. Cancer, 2015, 1856(2), 211-225. doi: 10.1016/j.bbcan.2015.08.003 PMID: 26318196
  78. Pietrobono, S.; Stecca, B. Aberrant sialylation in cancer: Biomarker and potential target for therapeutic intervention? Cancers, 2021, 13(9), 2014. doi: 10.3390/cancers13092014 PMID: 33921986
  79. Akella, N.M.; Le Minh, G.; Ciraku, L.; Mukherjee, A.; Bacigalupa, Z.A.; Mukhopadhyay, D.; Sodi, V.L.; Reginato, M.J. O-GlcNAc transferase regulates cancer stem–like potential of breast cancer cells. Mol. Cancer Res., 2020, 18(4), 585-598. doi: 10.1158/1541-7786.MCR-19-0732 PMID: 31974291
  80. Ma, Z.; Vosseller, K. Cancer metabolism and elevated O-GlcNAc in oncogenic signaling. J. Biol. Chem., 2014, 289(50), 34457-34465. doi: 10.1074/jbc.R114.577718 PMID: 25336642
  81. Lam, C.; Low, J.Y.; Tran, P.T.; Wang, H. The hexosamine biosynthetic pathway and cancer: Current knowledge and future therapeutic strategies. Cancer Lett., 2021, 503, 11-18. doi: 10.1016/j.canlet.2021.01.010 PMID: 33484754
  82. DeVito, S.R.; Ortiz-Riaño, E.; Martínez-Sobrido, L.; Munger, J. Cytomegalovirus-mediated activation of pyrimidine biosynthesis drives UDP–sugar synthesis to support viral protein glycosylation. Proc. Natl. Acad. Sci. USA, 2014, 111(50), 18019-18024. doi: 10.1073/pnas.1415864111 PMID: 25472841
  83. Hulikova, K.; Benson, V.; Svoboda, J.; Sima, P.; Fiserova, A. N-Acetyl-D-glucosamine-coated polyamidoamine dendrimer modulates antibody formation via natural killer cell activation. Int. Immunopharmacol., 2009, 9(6), 792-799. doi: 10.1016/j.intimp.2009.03.007 PMID: 19303462
  84. Han, T.; Kang, D.; Ji, D.; Wang, X.; Zhan, W.; Fu, M.; Xin, H.B.; Wang, J.B. How does cancer cell metabolism affect tumor migration and invasion? Cell Adhes. Migr., 2013, 7(5), 395-403. doi: 10.4161/cam.26345 PMID: 24131935
  85. Denning, T.L.; Takaishi, H.; Crowe, S.E.; Boldogh, I.; Jevnikar, A.; Ernst, P.B. Oxidative stress induces the expression of Fas and Fas ligand and apoptosis in murine intestinal epithelial cells. Free Radic. Biol. Med., 2002, 33(12), 1641-1650. doi: 10.1016/S0891-5849(02)01141-3 PMID: 12488132
  86. Brunk, U.T.; Svensson, I. Oxidative stress, growth factor starvation and Fas activation may all cause apoptosis through lysosomal leak. Redox Rep., 1999, 4(1-2), 3-11. doi: 10.1179/135100099101534675 PMID: 10714269

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