Mass spectrometric profiling reveals association of N-glycan patterns with epithelial ovarian cancer progression.

Abstract

Aberrant changes of N-glycan modifications on proteins have been linked to various diseases including different cancers, suggesting possible avenue for exploring their etiologies based on N-glycomic analysis. Changes in N-glycan patterns during epithelial ovarian cancer development have so far been investigated mainly using serum, plasma, ascites, and cell lines. However, changes in patterns of N-glycans in tumor tissues during epithelial ovarian cancer progression have remained largely undefined. To investigate whether changes in N-glycan patterns correlate with oncogenesis and progression of epithelial ovarian cancer, we profiled N-glycans from formalin-fixed paraffin-embedded tissue slides using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and quantitatively compared among different pathological grades of epithelial ovarian cancer and healthy controls. Our results show that among the 80 compositions of N-glycan detected, expression levels of high-mannose type were higher in epithelial ovarian cancer samples than that observed in healthy controls, accompanied by reduced levels of hybrid-type glycans. By applying receiver operating characteristic analysis, we show that a combined panel composed of four high-mannose and three fucosylated neutral complex N-glycans allows for good discrimination of epithelial ovarian cancer from healthy controls. Furthermore, using a statistical analysis of variance assay, we found that different N-glycan patterns, including 2 high-mannose-type, 2 fucosylated and sialylated complex structures, and 10 fucosylated neutral complex N-glycans, exhibited specific changes in N-glycan abundance across epithelial ovarian cancer grades. Together, our results provide strong evidence that N-glycomic changes are a strong indicator for epithelial ovarian cancer pathological grades and should provide avenues to identify novel biomarkers for epithelial ovarian cancer diagnosis and monitoring.