Characterization of IgG N-glycome profile in colorectal cancer progression by MALDI-TOF-MS.

Colorectal cancer (CRC) has become one of the most common cancers worldwide and the fifth most prevalent cancer in China with an upward trend in incidence rates. Altered glycosylation significantly affects the structural and functional changes in immunoglobulin G (IgG) and was consequently associated with disease progression. In this study, we explored the association of glycosylation with CRC prognosis through characterizing serum IgG N-glycans derived from individuals consisting of normal, benign colorectal and CRC cohorts in discovery set. Statistical analysis showed nine of IgG N-glycans were differentially expressed in disease groups compared with controls. Additionally, five out of them were still significantly changed in CRC patients at all tumor node metastasis (TNM) stages as compared with controls. Principal component analysis (PCA) indicated obvious differentiation of benign and cancer patients from normal individuals. Further diagnostic performance of receiver operator curve (ROC) analysis demonstrated at least moderately accurate area under the curve (AUC) score with preferable sensitivity and specificity, suggesting these five IgG N-glycans were probably correlated with CRC progression. Significantly, this result has been verified in validation set. Moreover, IgG N-glycosylation analysis suggested that core-fucosylation, sialylation and sialo core-fucosylation were possibly related to the development of CRC. SIGNIFICANCE: In-depth IgG N-glycome profiling of colorectal benign patients, colorectal cancer and normal individuals reveals differentially expression levels of N-glycosylation. Differing from serum comprehensive glycomes, profiling of specific serum glycoprotein contributes to more detailed understanding of biological relevance of glycosylation alterations in disease prognosis. Additionally, the high-throughput technique, MADLI-TOF-MS could absolutely relieve manual stress from sample preparation and accelerate information acquisition as compared to another recent analytical method. Moreover, we introduced a fast and easy data processing of MS exported files based on a software solution, which had the advantage of avoiding time consuming in manually searching and calculating the interesting peaks, and automatically producing the average percentage of each glycan over usual operation with Microsoft Excel. Besides, it may be useful for large-scale study.