A glycosylation-related gene signature predicts prognosis, immune microenvironment infiltration, and drug sensitivity in glioma - Yang, Yanbo; Teng, Haiying; Zhang, Yulian; Wang, Fei; Tang, Liyan; Zhang, Chuanpeng; Hu, Ziyi; Chen, Yuxuan; Ge, Yi; Wang, Zhong; Yu, Yanbing (Frontiers in Pharmacology, 2024)
Glioma represents the most common primary cancer of the central nervous system in adults. Glycosylation is a prevalent post-translational modification that occurs in eukaryotic cells, leading to a wide array of modifications on proteins. We obtained the clinical information, bulk RNA-seq data and single-cell RNA sequencing (scRNA-seq) from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), Gene Expression Omnibus (GEO) and Repository of Molecular Brain Neoplasia Data (Rembrandt) databases. RNA sequencing data for normal brain tissues were accessed from The Genotype-Tissue Expression (GTEx) database. Then, the glycosylation genes differentially expressed were identified and further passed to a least absolute shrinkage and selection operator (lasso) regularized cox model for variable selection. We further conducted enrichment analysis, qPCR, nomogram and single cell transcriptome to detect the glycosylation signature. Drug sensitivity analysis was also conducted. A five-gene glycosylation signature (CHPF2, PYGL, GALNT13, EXT2, COLGALT2) classified patients into low- or high-risk groups. Survival analysis, qPCR, ROC curves, and stratified analysis revealed worse outcomes in the high-risk group. Furthermore, GSEA and analysis of immune infiltration indicate that the glycosylation signature has potential for predicting the immune response in glioma.. After that, four drugs (Crizotinib, Lapatinib, Nilotinib, Topotecan) show different response between the two risk groups. Glioma cells had been classified into seven lines based on single cell expression profiles. The five-gene glycosylation signature can accurately predict the prognosis of glioma and may offer additional guidance for immunotherapy.