Machine learning-based identification of glycosyltransferase-related mRNAs for improving outcomes and the anti-tumor therapeutic response of gliomas - Zhang, Chunyu; Zhou, Wei (Frontiers in Pharmacology, 2023)
Background Glycosyltransferase participates in glycosylation modification, and glycosyltransferase alterations are involved in carcinogenesis, progression, and immune evasion, leading to poor outcomes. However, in-depth studies on the influence of glycosyltransferase on clinical outcomes and treatments are lacking.Methods Differentially expressed genes (DEGs) analysis was performed using the Gene Expression Profiling Interactive Analysis 2 (GEPIA2) database. 10 machine learning algorithms were introduced, including random survival forest (RSF), elastic network (Enet), least absolute shrinkage and selection operator (Lasso), Ridge, stepwise Cox, CoxBoost, partial least squares regression for Cox (plsRcox), supervised principal components (SuperPC), generalized boosted regression modelling (GBM), and survival support vector machine (survival-SVM). The Gene Set Enrichment Analysis (GSEA) was performed to explore signaling pathways regulated by the signature. Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was used for estimating the fractions of immune cell types.Here, we analyzed the genomic and expressive alterations of glycosyltransferase-related genes in glioma. The combination of 80 machine-learning algorithms was introduced to establish the glycosyltransferase-related mRNA signature (GRMS), based on 2,030 glioma samples from The Cancer Genome Atlas Program (TCGA), Chinese Glioma Genome Atlas (CGGA), Rembrant, Gravendeel and Kamoun cohorts. GRMS was identified as an independent hazardous factor for overall survival (OS) and demonstrated stable and robust performance. Notably, gliomas in the high-GRMS subgroup exhibited abundant tumor-infiltrating lymphocytes (TILs), tumor mutation burden (TMB) values, increased expressive levels of hepatitis A virus cellular receptor 2 (HAVCR2) and CD274 and improved progression-free survival (PFS) when treated with anti-tumor immunotherapy.It is possible that GRMS act as a powerful and promising biomarker for improving the clinical prognosis of glioma patients.