【摘要】 目的 建立口腔鳞状细胞癌（oral squamous cell carcinoma,OSCC）双硫死亡相关lncRNA临床预后模型，以评估患者预后情况。方法 首先从TCGA数据库中下载OSCC基因表达数据、临床信息及肿瘤负荷突变数据。从文献中手动编译10个双硫死亡相关基因，随后确定差异表达的双硫死亡相关lncRNA，通过单因素Cox回归分析、Lasso-Cox回归分析以及多因素Cox回归分析建立双硫死亡相关lncRNA临床预后模型，并验证预后模型的预测性能。结果 筛选出了7个建模lncRNA(AC090587.1、AC040977.1、AC008763.1、AL132800.1、AC092368.3、AL590617.2和VPS9D1-AS1)，构建了临床预后模型。低风险组和高风险组的总生存率差异显著（P<0.001）。单因素和多因素Cox回归分析证明该预后模型的风险评分是独立预后因素（P<0.001）。ROC曲线分析（AUC=0.699）表明了预后模型的较好性能。同时进行了相关基因的富集分析、免疫浸润相关分析及肿瘤突变负荷分析。结论 成功建立了口腔鳞状细胞癌双硫死亡相关lncRNA临床预后模型，它可帮助我们寻找OSCC的新型生物标志物和治疗靶点。更多还原

【Abstract】 Objective To establish a clinical prognostic model of lnc RNA associated with disulfide death in oralsquamous cell carcinoma(OSCC), aiming to assess patient prognosis.MethodsOSCC gene expression data, clinicalinformation, and tumor burden mutation data were downloaded from the TCGA database. Ten genes related todisulfide death were compiled from the literature, and differentially expressed dual sulfur death-associatedlnc RNAs were identified. A clinical prognostic model was established using univariate Cox regression analysis,Lasso-Cox regression analysis, and multivariate Cox regression analysis. The predictive performance of theprognostic model was validated.ResultsSeven modeling lnc RNAs(AC090587.1, AC040977.1, AC008763.1,AL132800.1, AC092368.3, AL590617.2, and VPS9D1-AS1) were selected to construct the clinical prognostic model. Theoverall survival difference between the low-risk and high-risk groups was significant(P<0.001). Univariate and multivariateCox regression analysis demonstrated that the risk score of the prognostic model was an independent prognostic factor(P<0.001). The ROC curve analysis(AUC=0.699) indicated good performance of the prognostic model. At the same time, theenrichment analysis of related genes, immune invasion correlation analysis and tumor mutation burden analysis were carriedout.ConclusionA successful clinical prognostic model of lnc RNA associated with disulfide mediated cell deathin oral squamous cell carcinoma has been established, which can help us identify novel biomarkers andtherapeutic targets for OSCC.更多还原