Construction of an abnormal glycosylation risk model and its application in predicting the prognosis of patients with head and neck cancer

Abstract

AbstractHead and neck squamous cell carcinoma (HNSCC) is the most common malignant tumor of the head and neck, and the incidence rate is increasing year by year. Protein post-translational modification, recognized as a pivotal and extensive form of protein modification, has been established to possess a profound association with tumor occurrence and progression. This study employed bioinformatics analysis utilizing transcriptome sequencing data, patient survival data, and clinical data from HNSCC to establish predictive markers of genes associated with glycosylation as prognostic risk markers. The R procedure WGCNA was employed to construct a gene co-expression network using the gene expression profile and clinical characteristics of HNSCC samples. Multiple Cox Proportional Hazards Regression Model (Cox regression) and LASSO analysis were conducted to identify the key genes exhibiting the strongest association with prognosis. A risk score, known as the glycosylation-related genes risk score (GLRS), was subsequently formulated utilizing the aforementioned core genes. This scoring system facilitated the classification of samples into high-risk and low-risk categories, thereby enabling the prediction of patient prognosis. The association between GLRS and clinical variables was examined through both univariate and multivariate Cox regression analysis. The validation of six core genes was accomplished using quantitative real-time polymerase chain reaction (qRT-PCR). The findings demonstrated noteworthy variations in risk scores among subgroups, thereby affirming the efficacy of GLRS in prognosticating patient outcomes. Furthermore, a correlation has been observed between the risk-scoring model and immune infiltration. Moreover, significant disparities exist in the expression levels of diverse immune checkpoints, epithelial-mesenchymal transition genes, and angiogenic factors between the high and low-risk groups.