Glycosylation modification identifies novel molecular phenotypes and prognostic stratifications of glioma

Abstract

Glycosylation modification plays a vital role in tumor progression and is highly associated with glioma prognosis. However, the influence of glycosylation modification on the tumor microenvironment (TME) and omic features of glioma remains unclear. Differentially expressed glycosylation-related genes between adjacent and tumor tissues of The Cancer Genome Atlas and Chinese Glioma Genome Atlas datasets were identified. We performed unsupervised clustering to classify patients into different molecular phenotypes, and analyzed their TME heterogeneity, including immunocyte infiltration, immune pathways and tumor purity. Subsequently, we developed a prognostic predicting system named GlycoScore by stepwise least absolute shrinkage and selection operator-Cox regression to evaluate the modification pattern and its association with somatic mutation, clinical significance, immune fractions and drug resistance. Two clustering clusters were identified and presented distinct clinical outcomes and biological functions characterized by hotand cold tumors respectively. Patients with higher GlycoScores exhibited poor prognosis, less mutation counts, and were more sensitive to chemotherapeutics. We also confirmed that the GlycoScore severed as an independent risk factor. Cancer hallmarks such as cell cycle, hippo, and TGFβ were active in the high-GlycoScore group. The combination of tumor mutation burden and the GlycoScore presented an excellent performance in prognostic stratification. Our study suggests that glycosylation is essential for modeling TME of glioma and the GlycoScore is a promising prognostic signature and indicator of immunotherapeutic efficacy.