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Analytical Pipeline for Discovery and Verification of Glycoproteins from Plasma-Derived Extracellular Vesicles as Breast Cancer Biomarkers.

I-Hsuan ChenHillary Andaluz AguilarJ Sebastian Paez PaezXiaofeng WuLi PanMichael K WendtAnton B IliukYing ZhangWeiguo Andy Tao
Published in: Analytical chemistry (2018)
Glycoproteins comprise more than half of current FDA-approved protein cancer markers, but the development of new glycoproteins as disease biomarkers has been stagnant. Here we present a pipeline to develop glycoproteins from extracellular vesicles (EVs) through integrating quantitative glycoproteomics with a novel reverse phase glycoprotein array and then apply it to identify novel biomarkers for breast cancer. EV glycoproteomics show promise in circumventing the problems plaguing current serum/plasma glycoproteomics and allowed us to identify hundreds of glycoproteins that have not been identified in blood. We identified 1,453 unique glycopeptides representing 556 glycoproteins in EVs, among which 20 were verified significantly higher in individual breast cancer patients. We further applied a novel glyco-specific reverse phase protein array to quantify a subset of the candidates. Together, this study demonstrates the great potential of this integrated pipeline for biomarker discovery.
Keyphrases
  • machine learning
  • big data
  • high throughput
  • small molecule
  • high resolution
  • deep learning
  • mental health
  • risk assessment
  • young adults
  • human health
  • childhood cancer
  • lymph node metastasis