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Comparative Glycoproteomic Profiling of Human Body Fluid between Healthy Controls and Patients with Papillary Thyroid Carcinoma

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posted on 19.12.2019, 16:39 by Yong Zhang, Wanjun Zhao, Yang Zhao, Yonghong Mao, Tao Su, Yi Zhong, Shisheng Wang, Rui Zhai, Jingqiu Cheng, Xiang Fang, Jingqiang Zhu, Hao Yang
Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer among women worldwide. It is confirmed mainly by fine-needle aspiration biopsy (FNAB), an invasive diagnostic method. The key proteins responsible for thyroid hormone biosynthesis are glycosylated. Hence, changes in site-specific glycosylation are associated with thyroid cancer. Integrated quantitative proteomic and glycoproteomic analyses of body fluids from patients with PTC may identify potential noninvasive biomarkers, improve diagnostic accuracy, and elucidate the basic mechanisms of tumor development. In the present study, we demonstrate an integrated, highly reproducible, rapid method involving body fluid proteome and glycoproteome analysis. Moreover, this method may quantitatively profile protein glycosylation. Intact N-glycopeptides from the urine and plasma of healthy controls (HC), PTC, and PTC with Hashimoto’s thyroiditis (PHT) were enriched by hydrophilic interaction liquid chromatography. Sialic acid was removed from the N-glycopeptides with trifluoroacetic acid and heat. The desialo-N-glycopeptides were analyzed by HCD-MS/MS using stepped collision energies and several search engines for quantitative profiling. Ninety-two altered proteins and 134 intact N-glycopeptides were isolated from the plasma and urine samples of the three groups (90 samples from 15 subjects). To the best of our knowledge, this study is the first to compare the plasma and urinary proteomes and glycoproteomes of HC, PTC, and PHT. Moreover, we reveal a novel indicator (ratio of fucosylated to nonfucosylated N-glycopeptide or F/NF) through desialo-N-glycopeptide analysis. These differently expressed glycoproteins and F/NF may serve as biomarkers contributing to clinical cancer diagnostics and could be used to improve diagnostic accuracy noninvasively.