Identification of Immune-Related Prognostic Biomarkers Associated with HPV-Positive Head and Neck Squamous Cell Carcinoma

Background. As a type of malignant tumor, head and neck squamous cell carcinoma (HNSCC) seriously threatens human health. This study is aimed at constructing a new, reliable prognostic model. Method. The gene expression profile data of HNSCC patients were downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas databases. The immune-related differentially expressed genes (IRDEGs) related to HNSCC were identified. We then used Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis to explore IRDEGs related to the HNSCC prognosis and to construct and validate a risk scoring model and used ESTIMATE to evaluate tumor immune infiltration in HNSCC patients. Finally, we validated IGSF5 expression and function in HNSCC cells. Results. A total of 1,195 IRDEGs were found from the GSE65858 dataset. Thirty-one of the 1,195 IRDEGs were associated with the prognosis of HNSCC. Nine key IRDEGs were further selected using the LASSO method, and a risk scoring model was established for predicting the survival of HNSCC patients. According to the risk scoring model, the prognosis of patients in the high-risk group was worse than that of the low-risk group; the high-risk group had significantly higher immune scores than the low-risk group; and between the high- and low-risk samples, there were significant differences in the proportion of 10 types of cells, including naive cells, plasma cells, and resting CD4(+) memory T cells. IGSF5 has low expression in HNSCC, and overexpression of IGSF5 significantly impaired HNSCC cell proliferation. Conclusion. This prognostic risk assessment model can help systematically evaluate the survival prognosis of HNSCC patients and provides a new research direction for the improvement of the survival prognosis of HNSCC patients in clinical practice.

Automated summary

This study aimed to construct a new prognostic model for HNSCC by analyzing gene expression data and immune-related genes. Through Cox regression and LASSO analysis, a risk scoring model was established to predict patient survival. Experimental results showed that the high-risk group had worse prognosis, higher immune scores, and significant differences in cell types compared to the low-risk group.