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Glycoproteomics-based signatures for tumor subtyping and clinical outcome prediction of high-grade serous ovarian cancer

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  • Jianbo Pan

    (School of Medicine
    School of Medicine)

  • Yingwei Hu

    (School of Medicine)

  • Shisheng Sun

    (School of Medicine)

  • Lijun Chen

    (School of Medicine)

  • Michael Schnaubelt

    (School of Medicine)

  • David Clark

    (School of Medicine)

  • Minghui Ao

    (School of Medicine)

  • Zhen Zhang

    (School of Medicine)

  • Daniel Chan

    (School of Medicine)

  • Jiang Qian

    (School of Medicine)

  • Hui Zhang

    (School of Medicine)

Abstract

Inter-tumor heterogeneity is a result of genomic, transcriptional, translational, and post-translational molecular features. To investigate the roles of protein glycosylation in the heterogeneity of high-grade serous ovarian carcinoma (HGSC), we perform mass spectrometry-based glycoproteomic characterization of 119 TCGA HGSC tissues. Cluster analysis of intact glycoproteomic profiles delineates 3 major tumor clusters and 5 groups of intact glycopeptides. It also shows a strong relationship between N-glycan structures and tumor molecular subtypes, one example of which being the association of fucosylation with mesenchymal subtype. Further survival analysis reveals that intact glycopeptide signatures of mesenchymal subtype are associated with a poor clinical outcome of HGSC. In addition, we study the expression of mRNAs, proteins, glycosites, and intact glycopeptides, as well as the expression levels of glycosylation enzymes involved in glycoprotein biosynthesis pathways in each tumor. The results show that glycoprotein levels are mainly controlled by the expression of their individual proteins, and, furthermore, that the glycoprotein-modifying glycans correspond to the protein levels of glycosylation enzymes. The variation in glycan types further shows coordination to the tumor heterogeneity. Deeper understanding of the glycosylation process and glycosylation production in different subtypes of HGSC may provide important clues for precision medicine and tumor-targeted therapy.

Suggested Citation

  • Jianbo Pan & Yingwei Hu & Shisheng Sun & Lijun Chen & Michael Schnaubelt & David Clark & Minghui Ao & Zhen Zhang & Daniel Chan & Jiang Qian & Hui Zhang, 2020. "Glycoproteomics-based signatures for tumor subtyping and clinical outcome prediction of high-grade serous ovarian cancer," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-19976-3
    DOI: 10.1038/s41467-020-19976-3
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    Cited by:

    1. Siyuan Kong & Pengyun Gong & Wen-Feng Zeng & Biyun Jiang & Xinhang Hou & Yang Zhang & Huanhuan Zhao & Mingqi Liu & Guoquan Yan & Xinwen Zhou & Xihua Qiao & Mengxi Wu & Pengyuan Yang & Chao Liu & Weiqi, 2022. "pGlycoQuant with a deep residual network for quantitative glycoproteomics at intact glycopeptide level," Nature Communications, Nature, vol. 13(1), pages 1-17, December.

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