Library-based virtual match-between-runs quantification in GlyPep-Quant improves site-specific glycan identification

He Zhu, Zheng Fang, Lei Liu, Yan Wang, Hongqiang Qin, Yongzhan Nie (), Mingming Dong () and Mingliang Ye ()
Additional contact information
He Zhu: Chinese Academy of Sciences
Zheng Fang: Chinese Academy of Sciences
Lei Liu: Chinese Academy of Sciences
Yan Wang: Chinese Academy of Sciences
Hongqiang Qin: Chinese Academy of Sciences
Yongzhan Nie: Fourth Military Medical University
Mingming Dong: Dalian University of Technology
Mingliang Ye: Chinese Academy of Sciences

Nature Communications, 2025, vol. 16, issue 1, 1-17

Abstract: Abstract Glycosylation changes are closely related to various diseases, including cancer. The quantitative analysis of site-specific glycans at proteomics scale remains challenging due to low glycopeptide spectra interpretation. Here, we present GlyPep-Quant, a tool for sensitive quantification and identification of site-specific glycans. Using a well-trained machine learning model, GlyPep-Quant quantified 25.1%–178.9% more site-specific glycans without missing values than pGlycoQuant, MSFragger-Glyco, and Skyline. To utilize identified information from previous large-scale dataset, an MS1 feature library-based “virtual match-between-runs” quantification scheme was developed, enabling over eightfold more site-specific glycan identification/quantification than conventional MS2-based methods. Enhanced coverage prompted the development of a glycoproteomic biomarker discovery method, involving calculation of site-specific glycan abundances ratios at the same glycosylation site, minimizing individual expression and experimental condition variability. Two pairs of site-specific glycan ratios on sites P01011-N127 and P08185-N96, were selected as high-performance biomarkers to classify gastric cancer (GC) from healthy controls (AUC > 0.95). Moreover, the two ratios performed well in distinguishing GC using an independent cohort by the library-based quantification strategy with diagnostic accuracy up to 85%. GlyPep-Quant is poised for broader glycoproteomic applications.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-61673-6 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-61673-6

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-61673-6

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().