A comprehensive analysis of subclass-specific IgG glycosylation in colorectal cancer progression by nanoLC-MS/MS

Abstract

Colorectal cancer is associated with changed IgG glycosylation, but the alteration in specific subclasses of IgG is unknown. Initially, we optimized five common IgG glycopeptide enrichment methods to acquire a comprehensive profile of IgG glycopeptides. However, an incomplete tryptic digestion of IgG occurred when using an ordinary protease to protein ratio, which significantly impacted the final statistical analysis. Herein, we introduced a two-step enzymatic digestion, enabling the complete digestion of IgG glycopeptides and further improving the detection intensity of the target glycopeptides. In order to rapidly process and automatically integrate the MS data, we developed a simple and effective code using MATLAB. Following statistical analysis, we observed that IgG1_H3N4F1 and IgG1_H3N4 were substantially increased in CRC, while IgG1_H5N5F1, IgG1_H5N4F1S1 and IgG2_H5N4F1 were markedly decreased. A further evaluation of the diagnostic performance showed that they all achieved a fair performance in discriminating the patients from the normal. In terms of the glycan features, it was demonstrated that the CRC progression was associated with increased agalactosylation, and the decreased digalactosylation and galactosylation per antenna on the diantenna glycans of IgG1 and IgG2. Concurrently, the decreased sialylation of IgG1 was strongly correlated with CRC. Moreover, an analysis of tumor-specific glycosylation showed that the alterations of IgG glycosylation were more significant in colon cancer, and no obvious difference was observed between colon and rectal cancer. This study comprehensively optimized the glycopeptide enrichment methods, evaluated the enzymatic digestion effect, and explored the association between CRC progression and subclass-specific glycosylation.