Backgroud: The stratification of head and neck squamous cell carcinoma (HNSCC) patients based on prognostic differences is critical for therapeutic guidance. This study was designed to construct a predictive signature derived from T-cell receptor-related genes (TCRRGs) to forecast the clinical outcomes in HNSCC. Methods: We sourced gene expression profiles from The Cancer Genome Atlas (TCGA) HNSCC dataset, GSE41613, and GSE65858 datasets. Utilizing consensus clustering analysis, we identified two distinct HNSCC clusters according to TCRRG expression. A TCRRG-based signature was subsequently developed and validated across diverse independent HNSCC cohorts. Moreover, we established a nomogram model based on TCRRGs. We further explored differences in immune landscapes between high- and low-risk groups. Results: The TCGA HNSCC dataset was stratified into two clusters, displaying marked variations in both overall survival (OS) and immune cell infiltration. Furthermore, we developed a robust prognostic signature based on TCRRG utilizing the TCGA HNSCC train cohort, and its prognostic efficacy was validated in the TCGA HNSCC test cohort, GSE41613, and GSE65858. Importantly, the high-risk group was characterized by a suppressive immune microenvironment, in contrast to the low-risk group. Our study successfully developed a robust TCRRG-based signature that accurately predicts clinical outcomes in HNSCC, offering valuable strategies for improved treatments.
背景：基于预后差异对头颈部鳞状细胞癌（HNSCC）患者进行分层对治疗指导至关重要。本研究旨在构建一种基于T细胞受体相关基因（TCRRG）的预测特征，以评估HNSCC的临床结局。方法：我们从癌症基因组图谱（TCGA）HNSCC数据集、GSE41613和GSE65858数据集中获取基因表达谱。通过一致性聚类分析，我们根据TCRRG表达识别出两个不同的HNSCC亚群。随后，我们开发了一种基于TCRRG的特征，并在多个独立的HNSCC队列中进行了验证。此外，我们基于TCRRG建立了列线图模型，并进一步探讨了高风险组与低风险组之间免疫景观的差异。结果：TCGA HNSCC数据集被分为两个亚群，显示出总生存期（OS）和免疫细胞浸润的显著差异。此外，我们利用TCGA HNSCC训练队列开发了一种基于TCRRG的稳健预后特征，并在TCGA HNSCC测试队列、GSE41613和GSE65858中验证了其预后效能。重要的是，与低风险组相比，高风险组表现出抑制性免疫微环境特征。本研究成功开发了一种稳健的基于TCRRG的特征，能够准确预测HNSCC的临床结局，为改善治疗提供了有价值的策略。
肿瘤(癌症)患者之家