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volume 21 issue 1 publication number 68

Quantitative site-specific N-glycosylation analysis reveals IgG glyco-signatures for pancreatic cancer diagnosis

YI JIN 1
RAN HU 1
Yufan Gu 1
Ailin Wei 2
Ang Li 1, 2
Yong Zhang 1
Publication typeJournal Article
Publication date2024-12-30
scimago Q1
wos Q2
SJR0.962
CiteScore4.3
Impact factor3.3
ISSN15426416, 15590275
Abstract
Pancreatic cancer is a highly aggressive tumor with a poor prognosis due to a low early detection rate and a lack of biomarkers. Most of pancreatic cancer is pancreatic ductal adenocarcinoma (PDAC). Alterations in the N-glycosylation of plasma immunoglobulin G (IgG) have been shown to be closely associated with the onset and development of several cancers and could be used as biomarkers for diagnosis. The study aimed to explore intact N-glycosylation profile of IgG in patients with PDAC and find relation between intact N-glycosylation profile of IgG and clinical information such as diagnosis and prognosis. In this study, we employed a well-evaluated approach (termed GlycoQuant) to assess the site-specific N-glycosylation profile of human plasma IgG in both healthy individuals and patients with pancreatic ductal adenocarcinoma (PDAC). The datasets generated and analyzed during the current study are available in the ProteomeXchange Consortium ( http://www.proteomexchange.org/ ) via the iProX partner repository, with the dataset identifier PXD051436. The analysis of rapidly purified IgG samples from 100 patients with different stages of PDAC, in addition to 30 healthy controls, revealed that the combination of carbohydrate antigen 19 − 9 (CA19-9), IgG1-GP05 (IgG1-TKPREEQYNSTYR-HexNAc [4]Hex [5]Fuc [1]NeuAc [1]), and IgG4-GP04 (IgG4-EEQFNSTYR- HexNAc [4]Hex [5]Fuc [1]NeuAc [1]) can be used to distinguish between PDAC patients and healthy individuals (AUC = 0.988). In addition, cross validation of the diagnosis model showed satisfactory result. The study demonstrated that the integrated quantitative method can be utilized for large-scale clinical N-glycosylation research to identify novel N-glycosylated biomarkers. This could facilitate the development of clinical glycoproteomics.
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JIN Y. et al. Quantitative site-specific N-glycosylation analysis reveals IgG glyco-signatures for pancreatic cancer diagnosis // Clinical Proteomics. 2024. Vol. 21. No. 1. 68
GOST all authors (up to 50) Copy
JIN Y., HU R., Gu Y., Wei A., Li A., Zhang Y. Quantitative site-specific N-glycosylation analysis reveals IgG glyco-signatures for pancreatic cancer diagnosis // Clinical Proteomics. 2024. Vol. 21. No. 1. 68
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RIS Copy
TY - JOUR
DO - 10.1186/s12014-024-09522-4
UR - https://clinicalproteomicsjournal.biomedcentral.com/articles/10.1186/s12014-024-09522-4
TI - Quantitative site-specific N-glycosylation analysis reveals IgG glyco-signatures for pancreatic cancer diagnosis
T2 - Clinical Proteomics
AU - JIN, YI
AU - HU, RAN
AU - Gu, Yufan
AU - Wei, Ailin
AU - Li, Ang
AU - Zhang, Yong
PY - 2024
DA - 2024/12/30
PB - Springer Nature
IS - 1
VL - 21
PMID - 39734184
SN - 1542-6416
SN - 1559-0275
ER -
BibTex
Cite this
BibTex (up to 50 authors) Copy
@article{2024_JIN,
author = {YI JIN and RAN HU and Yufan Gu and Ailin Wei and Ang Li and Yong Zhang},
title = {Quantitative site-specific N-glycosylation analysis reveals IgG glyco-signatures for pancreatic cancer diagnosis},
journal = {Clinical Proteomics},
year = {2024},
volume = {21},
publisher = {Springer Nature},
month = {dec},
url = {https://clinicalproteomicsjournal.biomedcentral.com/articles/10.1186/s12014-024-09522-4},
number = {1},
pages = {68},
doi = {10.1186/s12014-024-09522-4}
}