Abstract
Pancreatic cancer is among the most malignant cancers, and thus early intervention is the key to better survival outcomes. However, no methods have been derived that can reliably identify early precursors of development into malignancy. Therefore, it is urgent to discover early molecular changes during pancreatic tumorigenesis. As aberrant glycosylation is closely associated with cancer progression, numerous efforts have been made to mine glycosylation changes as biomarkers for diagnosis; however, detailed glycoproteomic information, especially site-specific N-glycosylation changes in pancreatic cancer with and without drug treatment, needs to be further explored. Herein, we used comprehensive solid-phase chemoenzymatic glycoproteomics to analyze glycans, glycosites, and intact glycopeptides in pancreatic cancer cells and patient sera. The profiling of N-glycans in cancer cells revealed an increase in the secreted glycoproteins from the primary tumor of MIA PaCa-2 cells, whereas human sera, which contain many secreted glycoproteins, had significant changes of glycans at their specific glycosites. These results indicated the potential role for tumor-specific glycosylation as disease biomarkers. We also found that AMG-510, a small molecule inhibitor against Kirsten rat sarcoma viral oncogene homolog (KRAS) G12C mutation, profoundly reduced the glycosylation level in MIA PaCa-2 cells, suggesting that KRAS plays a role in the cellular glycosylation process, and thus glycosylation inhibition contributes to the anti-tumor effect of AMG-510.
摘要
胰腺癌作为最恶性的癌症之一, 早期干预是提高生存率的关键. 目前尚无可靠的方法对其发展为恶性肿瘤进行早期识别, 因此在胰腺肿瘤发生过程中发现早期分子变化的要求迫在眉睫. 异常糖基化与癌症进展密切相关, 对将糖基化变化作为胰腺癌诊断的生物标记物已有较多研究, 但详细的糖组学信息, 尤其是胰腺癌在药物治疗前后的位点特异性N-糖基化变化研究, 仍需进一步深入. 本研究采用综合性固相化学酶糖组学手段, 对胰腺癌细胞和患者血清中的聚糖、 糖基化位点和完整糖肽展开分析. 癌症细胞中N-聚糖的分析结果显示, 原位肿瘤MIA PaCa-2细胞分泌的糖蛋白增加, 然而含有较多分泌糖蛋白的人类血清在其特定糖基化位点上的聚糖却发生了显著变化. 上述结果表明, 肿瘤特异性糖基化可作为胰腺癌诊断的潜在生物标志物. 此外, 本研究发现抗KRAS G12C突变的小分子抑制剂AMG-510可显著降低MIA PaCa-2细胞的糖基化水平, 这表明KRAS在细胞糖基化过程中发挥抑制作用, 将有助于AMG-510的抗肿瘤作用.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability statement
All data generated or analyzed during this study are included in this published article and its supplementary information files.
References
Abd-El-Halim YM, el Kaoutari A, Silvy F, et al., 2021. A glycosyltransferase gene signature to detect pancreatic ductal adenocarcinoma patients with poor prognosis. eBioMedicine, 71:103541. https://doi.org/10.1016/j.ebiom.2021.103541
Afshar-Kharghan V, 2017. The role of the complement system in cancer. J Clin Invest, 127(3):780–789. https://doi.org/10.1172/jci90962
Ardito CM, Grüner BM, Takeuchi KK, et al., 2012. EGF receptor is required for KRAS-induced pancreatic tumorigenesis. Cancer Cell, 22(3):304–317. https://doi.org/10.1016/j.ccr.2012.07.024
Bassagañas S, Carvalho S, Dias AM, et al., 2014. Pancreatic cancer cell glycosylation regulates cell adhesion and invasion through the modulation of α2β1 integrin and E-cadherin function. PLoS ONE, 9(5):e98595. https://doi.org/10.1371/journal.pone.0098595
Canon J, Rex K, Saiki AY, et al., 2019. The clinical KRAS (G12C) inhibitor AMG 510 drives anti-tumour immunity. Nature, 575(7781):217–223. https://doi.org/10.1038/s41586-019-1694-1
Cao LW, Lih TM, Hu YW, et al., 2022. Characterization of core fucosylation via sequential enzymatic treatments of intact glycopeptides and mass spectrometry analysis. Nat Commun, 13:3910. https://doi.org/10.1038/s41467-022-31472-4
Ceroni A, Maass K, Geyer H, et al., 2008. GlycoWorkbench: a tool for the computer-assisted annotation of mass spectra of glycans. J Proteome Res, 7(4):1650–1659. https://doi.org/10.1021/pr7008252
Chen HH, Deng ZA, Huang CC, et al., 2017. Mass spectrometric profiling reveals association of N-glycan patterns with epithelial ovarian cancer progression. Tumour Biol, 39(7):1010428317716249. https://doi.org/10.1177/1010428317716249
Chen J, Wu W, Chen LJ, et al., 2013. Profiling the potential tumor markers of pancreatic ductal adenocarcinoma using 2D-DIGE and MALDI-TOF-MS: up-regulation of Complement C3 and alpha-2-HS-glycoprotein. Pancreatology, 13(3):290–297. https://doi.org/10.1016/j.pan.2013.03.010
de Leoz MLA, Young LJT, An HJ, et al., 2011. High-mannose glycans are elevated during breast cancer progression. Mol Cell Proteomics, 10(1):M110.002717. https://doi.org/10.1074/mcp.M110.002717
Esmail S, Manolson MF, 2021. Advances in understanding N-glycosylation structure, function, and regulation in health and disease. Eur J Cell Biol, 100(7–8):151186. https://doi.org/10.1016/j.ejcb.2021.151186
Gao ZR, Ling XY, Shi CY, et al., 2022. Tumor immune checkpoints and their associated inhibitors. J Zhejiang Univ-Sci B (Biomed & Biotechnol), 23(10):823–843. https://doi.org/10.1631/jzus.B2200195
Gold G, Goh SK, Christophi C, et al., 2019. Dilemmas and limitations interpreting carbohydrate antigen 19-9 elevation after curative pancreatic surgery: a case report. Int J Surg Case Rep, 54:20–22. https://doi.org/10.1016/j.ijscr.2018.11.022
Holst S, Belo AI, Giovannetti E, et al., 2017. Profiling of different pancreatic cancer cells used as models for metastatic behaviour shows large variation in their N-glycosylation. Sci Rep, 7:16623. https://doi.org/10.1038/s41598-017-16811-6
Hruban RH, Petersen GM, Ha PK, et al., 1998. Genetics of pancreatic cancer: from genes to families. Surg Oncol Clin North Am, 7(1):1–23. https://doi.org/10.1016/S1055-3207(18)30282-5
Hu HF, Ye Z, Qin Y, et al., 2021. Mutations in key driver genes of pancreatic cancer: molecularly targeted therapies and other clinical implications. Acta Pharmacol Sin, 42(11):1725–1741. https://doi.org/10.1038/s41401-020-00584-2
Lee SJ, Evers S, Roeder D, et al., 2002. Mannose receptor-mediated regulation of serum glycoprotein homeostasis. Science, 295(5561):1898–1901. https://doi.org/10.1126/science.1069540
Levink IJM, Klatte DCF, Hanna-Sawires RG, et al., 2022. Longitudinal changes of serum protein N-Glycan levels for earlier detection of pancreatic cancer in high-risk individuals. Pancreatology, 22(4):497–506. https://doi.org/10.1016/j.pan.2022.03.021
Liang Y, Wang W, Fang C, et al., 2016. Clinical significance and diagnostic value of serum CEA, CA19-9 and CA72-4 in patients with gastric cancer. Oncotarget, 7(31):49565–49573. https://doi.org/10.18632/oncotarget.10391
Lin YS, Tamakoshi A, Kikuchi S, et al., 2004. Serum insulinlike growth factor-I, insulin-like growth factor binding protein-3, and the risk of pancreatic cancer death. Int J Cancer, 110(4):584–588. https://doi.org/10.1002/ijc.20147
Liu LY, Zhu B, Fang Z, et al., 2021. Automated intact glycopeptide enrichment method facilitating highly reproducible analysis of serum site-specific N-glycoproteome. Anal Chem, 93(20):7473–7480. https://doi.org/10.1021/acs.analchem.1c00645
Lu HR, Xiao KJ, Tian ZX, 2021. Benchmark of site- and structure-specific quantitative tissue N-glycoproteomics for discovery of potential N-glycoprotein markers: a case study of pancreatic cancer. Glycoconj J, 38(2):213–231. https://doi.org/10.1007/s10719-021-09994-8
Lumibao JC, Tremblay JR, Hsu J, et al., 2022. Altered glycosylation in pancreatic cancer and beyond. J Exp Med, 219(6):e20211505. https://doi.org/10.1084/jem.20211505
Marrelli D, Caruso S, Pedrazzani C, et al., 2009. CA19-9 serum levels in obstructive jaundice: clinical value in benign and malignant conditions. Am J Surg, 198(3):333–339. https://doi.org/10.1016/j.amjsurg.2008.12.031
Maxwell E, Tan Y, Tan YX, et al., 2012. GlycReSoft: a software package for automated recognition of glycans from LC/MS data. PLoS ONE, 7(9):e45474. https://doi.org/10.1371/journal.pone.0045474
Mofid MR, Gheysarzadeh A, Bakhtiyari S, 2020. Insulin-like growth factor binding protein 3 chemosensitizes pancreatic ductal adenocarcinoma through its death receptor. Pancreatology, 20(7):1442–1450. https://doi.org/10.1016/j.pan.2020.07.406
Munkley J, 2019. The glycosylation landscape of pancreatic cancer (Review). Oncol Lett, 17(3):2569–2575. https://doi.org/10.3892/ol.2019.9885
Narayanasamy A, Ahn JM, Sung HJ, et al., 2011. Fucosylated glycoproteomic approach to identify a complement component 9 associated with squamous cell lung cancer (SQLC). J Proteomics, 74(12):2948–2958. https://doi.org/10.1016/j.jprot.2011.07.019
Nie S, Lo A, Wu J, et al., 2014. Glycoprotein biomarker panel for pancreatic cancer discovered by quantitative proteomics analysis. J Proteome Res, 13(4):1873–1884. https://doi.org/10.1021/pr400967x
Pan S, Tamura Y, Chen R, et al., 2012. Large-scale quantitative glycoproteomics analysis of site-specific glycosylation occupancy. Mol Biosyst, 8(11):2850–2856. https://doi.org/10.1039/c2mb25268f
Pan S, Chen R, Tamura Y, et al., 2014. Quantitative glycoproteomics analysis reveals changes in N-glycosylation level associated with pancreatic ductal adenocarcinoma. J Proteome Res, 13(3):1293–1306. https://doi.org/10.1021/pr4010184
Park HM, Hwang MP, Kim YW, et al., 2015. Mass spectrometry-based N-linked glycomic profiling as a means for tracking pancreatic cancer metastasis. Carbohydr Res, 413:5–11. https://doi.org/10.1016/j.carres.2015.04.019
Preston RJS, Rawley O, Gleeson EM, et al., 2013. Elucidating the role of carbohydrate determinants in regulating hemostasis: insights and opportunities. Blood, 121(19):3801–3810. https://doi.org/10.1182/blood-2012-10-415000
Reily C, Stewart TJ, Renfrow MB, et al., 2019. Glycosylation in health and disease. Nat Rev Nephrol, 15(6):346–366. https://doi.org/10.1038/s41581-019-0129-4
Rho JH, Mead JR, Wright WS, et al., 2014. Discovery of sialyl Lewis A and Lewis X modified protein cancer biomarkers using high density antibody arrays. J Proteomics, 96:291–299. https://doi.org/10.1016/j.jprot.2013.10.030
Roopenian DC, Akilesh S, 2007. FcRn: the neonatal Fc receptor comes of age. Nat Rev Immunol, 7(9):715–725. https://doi.org/10.1038/nri2155
Ryan DP, Hong TS, Bardeesy N, 2014. Pancreatic adenocarcinoma. N Engl J Med, 371(11):1039–1049. https://doi.org/10.1056/NEJMra1404198
Sato Y, Fujimoto D, Uehara K, et al., 2016. The prognostic value of serum CA 19-9 for patients with advanced lung adenocarcinoma. BMC Cancer, 16:890. https://doi.org/10.1186/s12885-016-2897-6
Sethi MK, Hancock WS, Fanayan S, 2016. Identifying N-glycan biomarkers in colorectal cancer by mass spectrometry. Acc Chem Res, 49(10):2099–2106. https://doi.org/10.1021/acs.accounts.6b00193
Sung H, Ferlay J, Siegel RL, et al., 2021. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 71(3):209–249. https://doi.org/10.3322/caac.21660
Talar-Wojnarowska R, Gasiorowska A, Olakowski M, et al., 2011. Clinical value of serum neopterin, tissue polypeptide-specific antigen and CA19-9 levels in differential diagnosis between pancreatic cancer and chronic pancreatitis. Pancreatology, 10(6):689–694. https://doi.org/10.1159/000320693
Taparra K, Wang HL, Malek R, et al., 2018. O-GlcNAcylation is required for mutant KR4S-induced lung tumorigenesis. J Clin Invest, 128(11):4924–4937. https://doi.org/10.1172/JCI94844
Vreeker GCM, Hanna-Sawires RG, Mohammed Y, et al., 2020. Serum N-Glycome analysis reveals pancreatic cancer disease signatures. Cancer Med, 9(22):8519–8529. https://doi.org/10.1002/cam4.3439
Vukobrat-Bijedic Z, Husic-Selimovic A, Sofic A, et al., 2013. Cancer antigens (CEA and CA 19-9) as markers of advanced stage of colorectal carcinoma. Med Arch, 67(6):397–401. https://doi.org/10.5455/medarh.2013.67.397-401
Xiao HP, Sun FX, Suttapitugsakul S, et al., 2019. Global and site-specific analysis of protein glycosylation in complex biological systems with Mass Spectrometry. Mass Spectrom Rev, 38(4–5):356–379. https://doi.org/10.1002/mas.21586
Xu MM, Hu WH, Liu ZL, et al., 2021. Glycoproteomic bioanalysis of exosomes by LC–MS for early diagnosis of pancreatic cancer. Bioanalysis, 13(11):861–864. https://doi.org/10.4155/bio-2021-0036
Xu MM, Jin H, Wu Z, et al., 2022. Mass spectrometry-based analysis of serum N-glycosylation changes in patients with Parkinson’s disease. ACS Chem Neurosci, 13(12):1719–1726. https://doi.org/10.1021/acschemneuro.2c00264
Yang S, Li Y, Shah P, et al., 2013. Glycomic analysis using glycoprotein immobilization for glycan extraction. Anal Chem, 85(11):5555–5561. https://doi.org/10.1021/ac400761e
Yang S, Jankowska E, Kosikova M, et al., 2017. Solid-phase chemical modification for sialic acid linkage analysis: application to glycoproteins of host cells used in influenza virus propagation. Anal Chem, 89(17):9508–9517. https://doi.org/10.1021/acs.analchem.7b02514
Yang S, Wu WW, Shen RF, et al., 2018. Identification of sialic acid linkages on intact glycopeptides via differential chemical modification using intactGIG-HILIC. J Am Soc Mass Spectrom, 29(6):1273–1283. https://doi.org/10.1007/s13361-018-1931-0
Yang S, Xia J, Yang ZR, et al., 2021. Lung cancer molecular mutations and abnormal glycosylation as biomarkers for early diagnosis. Cancer Treat Res Commun, 27:100311. https://doi.org/10.1016/j.ctarc.2021.100311
Zhang H, Li XJ, Martin DB, et al., 2003. Identification and quantification of N-linked glycoproteins using hydrazide chemistry, stable isotope labeling and mass spectrometry. Nat Biotechnol, 21(6):660–666. https://doi.org/10.1038/nbt827
Zhang W, Wang YY, Dong X, et al., 2021. Elevated serum CA19-9 indicates severe liver inflammation and worse survival after curative resection in hepatitis B-related hepatocellular carcinoma. Biosci Trends, 15(6):397–405. https://doi.org/10.5582/bst.2021.01517
Zhao J, Qiu WL, Simeone DM, et al., 2007. N-linked glycosylation profiling of pancreatic cancer serum using capillary liquid phase separation coupled with mass spectrometric analysis. J Proteome Res, 6(3):1126–1138. https://doi.org/10.1021/pr0604458
Acknowledgments
This work was supported by the Soochow University Start-up Fund, the Priority Academic Program Development of the Jiangsu Higher Education Institutes (PAPD), the Jiangsu Science and Technology Plan Funding (No. BX2022023), the Jiangsu Shuangchuang Boshi Funding (No. JSSCBS20210697), and the Foundation of Zhejiang Provincial Administration of Traditional Chinese Medicine (No. 2020ZB020), China.
Author information
Authors and Affiliations
Contributions
Mingming XU, Zhaoliang LIU, Wenhua HU, and Shuang YANG contributed to data collection, analysis, interpretation, and writing of the manuscript. Sufeng CHEN, Peng XIA, and Jing DU contributed to sample collection. Ying HAN and Zhen WU contributed to mass spectrometry study. Mingming XU, Xumin ZHANG, Piliang HAO, Jun XIA, and Shuang YANG contributed to the study concept and design, study supervision, and critical revision of the manuscript. All authors have read and approved the final manuscript, and therefore, have full access to all the data in the study and take responsibility for the integrity and security of the data.
Corresponding authors
Ethics declarations
Mingming XU, Zhaoliang LIU, Wenhua HU, Ying HAN, Zhen WU, Sufeng CHEN, Peng XIA, Jing DU, Xumin ZHANG, Piliang HAO, Jun XIA, and Shuang YANG declare no competing interests.
This study was approved by the Research Ethics Committees of Zhejiang Provincial People’s Hospital (No. QT2022387). The written informed consent was provided to patients in advance.
Additional information
Supplementary information
Materials and methods; Tables S1–S6; Figs. S1–S3
Supplementary information
Rights and permissions
About this article
Cite this article
Xu, M., Liu, Z., Hu, W. et al. Mass spectrometry analysis of intact protein N-glycosylation signatures of cells and sera in pancreatic adenocarcinomas. J. Zhejiang Univ. Sci. B 25, 51–64 (2024). https://doi.org/10.1631/jzus.B2200652
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/jzus.B2200652