Miao Tingting, Si Qingzong, Wei Yuan, Fan Ruihong, Wang Junjie, An Xiaoli
School of Stomatology, Lanzhou University, Gansu, Lanzhou 730000, P.R. China.
Oncol Lett. 2020 Jul;20(1):939-946. doi: 10.3892/ol.2020.11603. Epub 2020 May 13.
Oral squamous cell carcinoma (OSCC) is one of the most common malignancies worldwide, due to poor diagnosis and treatment. There is increasing evidence that demonstrates the involvement of long non-coding RNAs (lncRNAs) in carcinogenesis and cancer progression. Therefore, the aim of the present study was to explore potential lncRNA-associated features of patients with OSCC as a valuable and independent prognostic biomarker. A total of 268 lncRNA expression profiles and clinical patient information on OSCC were downloaded from The Cancer Genome Atlas database. The clinical information was exploited for prescreening, using Cox regression analysis, and differentially expressed lncRNAs (DElncRNAs) were identified using edgeR software. Using the 'caret' package, the datasets were categorized into test datasets and training datasets, respectively. Through bioinformatics, seven prognostic DElncRNAs were selected. Using the regression coefficients, a risk score based on the seven-DElncRNA signature was developed to assess the prognostic function of key DElncRNAs. According to the median risk score, patients were classified into high-risk and low-risk groups in the training and test datasets. Additionally, receiver operating characteristic (ROC) curve analysis was conducted to evaluate the sensitivity and specificity of the prognostic DElncRNAs, and the optimal cut-off point was obtained from ROC analysis. Based on the optimal cut-off point, the patients were also categorized into high-risk and low-risk groups. Notably, the optimal cut-off point was more sensitive than the median risk score, particularly in the test dataset. The Kaplan-Meier survival and log rank test analysis results indicated that the P-value, based on the optimal cut-off, was less than the median risk cut-off. Additionally, stratified analysis results revealed that the seven-DElncRNAs signature was also independent of OSCC age. Furthermore, the findings of the present study suggested that the seven-DElncRNA signature can be used as a potential prognostic indicator and may have important clinical significance in OSCC.
口腔鳞状细胞癌（OSCC）是全球最常见的恶性肿瘤之一，原因在于诊断和治疗手段有限。越来越多的证据表明，长链非编码RNA（lncRNA）参与了肿瘤发生和癌症进展过程。因此，本研究的目的是探索OSCC患者潜在的lncRNA相关特征，作为一种有价值的独立预后生物标志物。从癌症基因组图谱数据库下载了总共268个OSCC的lncRNA表达谱和临床患者信息。利用Cox回归分析对临床信息进行预筛选，并使用edgeR软件识别差异表达的lncRNA（DElncRNA）。使用“caret”包将数据集分别分类为测试数据集和训练数据集。通过生物信息学，选择了7个预后DElncRNA。利用回归系数，开发了基于7个DElncRNA特征的风险评分，以评估关键DElncRNA的预后功能。根据中位风险评分，将训练和测试数据集中的患者分为高风险组和低风险组。此外，进行了受试者工作特征（ROC）曲线分析，以评估预后DElncRNA的敏感性和特异性，并从ROC分析中获得最佳截断点。基于最佳截断点，患者也被分为高风险组和低风险组。值得注意的是，最佳截断点比中位风险评分更敏感，尤其是在测试数据集中。Kaplan-Meier生存分析和对数秩检验分析结果表明，基于最佳截断点的P值小于中位风险截断值。此外，分层分析结果显示，7个DElncRNA特征也与OSCC患者年龄无关。此外，本研究结果表明，7个DElncRNA特征可作为潜在的预后指标，可能在OSCC中具有重要的临床意义。
