Fei Yinjiao, Liu Zhen, Yuan Jinling, Qiu Lei, Zhu Yuchen, Shi Kexin, Luo Jinyan, Wu Mengxing, Xu Weilin, Zhou Shu
Department of Radiation Therapy, The First Affiliated Hospital With Nanjing Medical University, 300 Guangzhou Road, Nanjing, 210029, People's Republic of China.
Department of General Surgery, The First Affiliated Hospital of Soochow University, No. 199 Ren'ai Road, Suzhou, 215021, People's Republic of China.
Discov Oncol. 2025 May 26;16(1):933. doi: 10.1007/s12672-025-02731-9.
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.
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.
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.
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.
放射抗性显著损害头颈部鳞状细胞癌（HNSCC）的治疗效果和预后结果。本研究旨在识别放疗敏感性相关基因，并构建HNSCC的预后模型，纳入鼻咽癌（NPC）这一相关亚型的见解。
使用主要来源于NPC的GSE48501数据集识别与放疗反应相关的差异表达基因（DEG）。通过基因本体论（GO）和京都基因与基因组百科全书（KEGG）通路分析进行功能注释。使用TCGA-HNSC数据集，我们通过单变量和LASSO-Cox回归分析开发了一个预后风险模型。使用CIBERSORT、TIMER、癌症药物敏感性基因组学（GDSC）和列线图分析对该模型的预后准确性进行验证，并进一步分析其与免疫细胞浸润、药物敏感性和生存结果的关联。
我们识别出263个与放疗敏感性相关的DEG，并基于8个枢纽基因构建了一个强大的预后模型。该模型有效地将患者分为高风险组和低风险组，低风险组观察到更好的总生存期（OS）。受试者工作特征（ROC）分析证实了该模型对1年、3年和5年OS具有较高的预测准确性。免疫浸润分析显示高风险组的免疫活性降低，而药物敏感性分析突出了潜在的治疗策略。列线图进一步证明了其优异的预测性能。
本研究将来自NPC衍生的DEG的见解与HNSCC预后建模相结合，强调放疗敏感性，并整合了免疫和治疗维度。所得模型为提高HNSCC患者的预后准确性和指导治疗策略提供了一种新方法。
