Abstract

Background: Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous cancer characterized by difficulties in early diagnosis and outcome prediction. Aberrant glycosylated structures produced by the aberrant expression of glycosyltransferases are prevalent in HNSCC. In this study, we aim to construct glycosyltransferase-related gene signatures with diagnostic and prognostic value to better stratify patients with HNSCC and improve their diagnosis and prognosis.

Methods: Bioinformatic tools were used to process data of patients with HNSCC from The Cancer Genome Atlas (TCGA) database. The prognostic model was formatted using univariate and multivariate Cox regression methods, while the diagnostic signature was constructed using support vector machine (SVM) and LASSO analysis. The results were verified using the Gene Expression Omnibus (GEO) cohort. The tumor microenvironment and benefits of immune checkpoint inhibitor (ICI) therapy in subgroups defined by glycosyltransferase-related genes were analyzed. Molecular biology experiments, including western blotting, cell counting kit (CCK)-8, colony formation, wound healing, and Transwell assays, were conducted to confirm the oncogenic function of beta-1,4-galactosyltransferase 3 (B4GALT3) in HNSCC.

Results: We established a five-gene prognostic signature and a 15-gene diagnostic model. Based on the median risk score, patients with low risk had longer overall survival than those in the high-risk group, which was consistent with the results of the GEO cohort. The concrete results suggested that high-risk samples were related to a high tumor protein (TP)53 mutation rate, high infiltration of resting memory cluster of differentiation (CD)4 T cells, resting natural killer (NK) cells, and M0 macrophages, and benefited from ICI therapy. In contrast, the low-risk subgroup was associated with a low TP53 mutation rate; and high infiltration of naive B cells, plasma cells, CD8 T cells, and resting mast cells; and benefited less from ICI therapy. In addition, the diagnostic model had an area under curve (AUC) value of 0.997 and 0.978 in the training dataset and validation cohort, respectively, indicating the high diagnostic potential of the model. Ultimately, the depletion of B4GALT3 significantly hindered the proliferation, migration, and invasion of HNSCC cells.

Conclusions: We established two new biomarkers that could provide clinicians with diagnostic, prognostic, and treatment guidance for patients with HNSCC.