Abstract
BACKGROUND: Radioresistance significantly impairs treatment efficacy and prognostic outcomes in head and neck squamous cell carcinoma (HNSCC). This study aimed to identify radiotherapy sensitivity-related genes and construct a prognostic model for HNSCC, incorporating insights from nasopharyngeal carcinoma (NPC) as a related subtype. METHODS: Differentially expressed genes (DEGs) associated with radiotherapy response were identified using the GSE48501 dataset, primarily derived from NPC. Functional annotation was performed via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Using the TCGA-HNSC dataset, we developed a prognostic risk model through univariate and LASSO-Cox regression analyses. The model was validated for prognostic accuracy and further analyzed for associations with immune cell infiltration, drug sensitivity, and survival outcomes using CIBERSORT, TIMER, Genomics of Drug Sensitivity in Cancer (GDSC), and nomogram analysis. RESULTS: We identified 263 DEGs related to radiotherapy sensitivity and developed a robust prognostic model based on 8 hub genes. The model effectively stratified patients into high- and low-risk groups, with superior overall survival (OS) observed in the low-risk group. The Receiver Operating Characteristic (ROC) analysis confirmed high predictive accuracy for 1-, 3-, and 5-year OS. Immune infiltration analysis revealed reduced immune activity in the high-risk group, while drug sensitivity analysis highlighted potential therapeutic strategies. The nomogram further demonstrated excellent predictive performance. CONCLUSION: This study bridges insights from NPC-derived DEGs and HNSCC prognostic modeling, emphasizing radiotherapy sensitivity and integrating immune and therapeutic dimensions. The resulting model offers a novel approach to improve prognostic accuracy and guide treatment strategies for HNSCC patients.