Table_1_Identification of a cuproptosis-related lncRNA signature to predict the prognosis and immune landscape of head and neck squamous cell carcinoma.xls

BackgroundCuproptosis is considered a novel copper-induced cell death model regulated by targeting lipoylated TCA cycle proteins. In this study, we established a novel signature based on cuproptosis-related lncRNAs (crlncRNAs) to predict the prognosis and immune landscape of head and neck squamous cell carcinoma.
MethodsRNA-seq matrix, somatic mutation files, and clinical data were obtained from The Cancer Genome Atlas database. After dividing patients into two sets, a crlncRNA signature was established based on survival related crlncRNAs, which were selected by the univariate Cox analysis and least absolute shrinkage and selection operator Cox regression. To evaluate the model, Kaplan-Meier survival analysis and time-dependent receiver operating characteristic (ROC) were utilized, and a nomogram was established for survival prediction. Immune landscape analysis, drug sensitivity, cluster analysis, tumor mutation burden (TMB) and ceRNA network analysis were conducted subsequently.
ResultsA crlncRNA related prognosis signature was finally constructed with 12 crlncRNAs. The areas under the ROC curves (AUCs) were 0.719, 0.705 and 0.693 respectively for 1, 3, and 5-year’s overall survival (OS). Patients in the low-risk group behaved a better prognosis, lower TMB, higher immune function activity and scores. In addition, patients from cluster 2 were more sensitive to chemotherapy and immunotherapy.
ConclusionIn this study, we constructed a novel crlncRNA risk model to predict the survival of HNSCC patients. This reliable and acceptable prognostic signature may guide and promote the progress of novel treatment strategies for HNSCC patients.

铜死亡（Cuproptosis）是一种新型的铜诱导细胞死亡模式，其调控机制靶向脂酰化三羧酸循环（TCA cycle）蛋白。本研究基于铜死亡相关长链非编码RNA（cuproptosis-related lncRNAs, crlncRNAs）构建了一种新型特征标签，用于预测头颈部鳞状细胞癌（head and neck squamous cell carcinoma, HNSCC）的预后与免疫微环境特征。
本研究从癌症基因组图谱（The Cancer Genome Atlas, TCGA）数据库获取了RNA测序（RNA-seq）矩阵、体细胞突变文件与临床数据。将患者划分为两个队列后，通过单因素Cox回归分析与最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）Cox回归筛选出与生存相关的crlncRNAs，以此构建crlncRNA特征标签。为评估该模型的性能，本研究采用Kaplan-Meier生存分析与时序受试者工作特征（receiver operating characteristic, ROC）曲线分析，并构建了用于生存预测的列线图（nomogram）。后续还开展了免疫微环境分析、药物敏感性分析、聚类分析、肿瘤突变负荷（tumor mutation burden, TMB）分析以及内源竞争RNA（competing endogenous RNA, ceRNA）网络分析。
本研究最终构建了包含12个crlncRNAs的预后特征标签。该标签对应1年、3年、5年总生存期（overall survival, OS）的受试者工作特征曲线下面积（areas under the ROC curves, AUCs）分别为0.719、0.705与0.693。低风险组患者的预后更佳，肿瘤突变负荷更低，免疫功能活性与免疫评分更高。此外，聚类2组的患者对化疗与免疫治疗更为敏感。
本研究构建了一种新型crlncRNA风险模型，可用于预测头颈部鳞状细胞癌患者的生存情况。该可靠且实用的预后特征标签有望为头颈部鳞状细胞癌患者的新型治疗策略提供指导并推动其发展。