Identification and validation of a prognostic risk model based on radiosensitivity-related genes in nasopharyngeal carcinoma

Background: Despite advancements with intensity-modulated radiation therapy (IMRT), about 10 % of nasopharyngeal carcinoma (NPC) patients remain resistant to radiotherapy, leading to recurrence and poor prognosis. This study aims to identify radiosensitivity-related genes in NPC and develop a prognostic model to predict patient outcomes. Methods: We analyzed 179 NPC samples from Fujian Cancer Hospital using RNA sequencing. Differentially expressed genes (DEGs) were identified between radiotherapy-sensitive and resistant samples. Machine learning algorithms and Cox regression were used to construct a prognostic risk model, validated in the GSE102349 dataset. Additional analyses included functional pathway, immune infiltration, and drug sensitivity. Results: A risk model based on six genes (LCN8, IGSF1, RIMS2, RBP4, TBX10, ETV4) was developed. Kaplan-Meier analysis showed significantly shorter progression-free survival (PFS) in the high-risk group. The model's AUC values were 0.872, 0.807, and 0.802 for 1-year, 3-year, and 5-year predictions. A nomogram including clinical factors was created, and enrichment analysis linked the high-risk group to radiotherapy resistance mechanisms. Conclusions: This study established a novel radiosensitivity-related prognostic model, offering insights into NPC prognosis and radiotherapy resistance mechanisms.