Department of Head and Neck Surgery, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, 510095, Guangdong Province, P.R. China.
Department of Pathology, Affiliated Cancer Hospital & Institute of Guangzhou Medical University, Guangzhou, 510095, Guangdong Province, P.R. China.
Sci Rep. 2019 Nov 25;9(1):17460. doi: 10.1038/s41598-019-53811-0.
Long non-coding RNAs (lncRNAs) which have little or no protein-coding capacity, due to their potential roles in the cancer disease, caught a particular interest. Our study aims to develop an lncRNAs-based classifier and a nomogram incorporating the lncRNAs classifier and clinicopathologic factors to help to improve the accuracy of recurrence prediction for head and neck squamous cell carcinoma (HNSCC) patients. The HNSCC lncRNAs profiling data and the corresponding clinicopathologic information were downloaded from TANRIC database and cBioPortal. Using univariable Cox regression and Least absolute shrinkage and selection operator (LASSO) analysis, we developed 15-lncRNAs-based classifier related to recurrence. On the basis of multivariable Cox regression analysis results, a nomogram integrating the genomic and clinicopathologic predictors was built. The predictive accuracy and discriminative ability of the inclusive nomogram were confirmed by calibration curve and a concordance index (C-index), and compared with TNM stage system by C-index, receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was conducted to evaluate clinical value of our nomogram. Consequently, fifteen recurrence-free survival (RFS) -related lncRNAs were identified, and the classifier consisting of the established 15 lncRNAs could effectively divide patients into high-risk and low-risk subgroup. The prediction ability of the 15-lncRNAs-based classifier for predicting 3- year and 5-year RFS were 0.833 and 0.771. Independent factors derived from multivariable analysis to predict recurrence were number of positive LNs, margin status, mutation count and lncRNAs classifier, which were all embedded into the nomogram. The calibration curve for the recurrence probability showed that the predictions based on the nomogram were in good coincide with practical observations. The C-index of the nomogram was 0.76 (0.72-0.79), and the area under curve (AUC) of nomogram in predicting RFS was 0.809, which were significantly higher than traditional TNM stage and 15-lncRNAs-based classifier. Decision curve analysis further demonstrated that our nomogram had larger net benefit than TNM stage and 15-lncRNAs-based classifier. The results were confirmed externally. In summary, a visually inclusive nomogram for patients with HNSCC, comprising genomic and clinicopathologic variables, generates more accurate prediction of the recurrence probability when compared TNM stage alone, but more additional data remains needed before being used in clinical practice.
长链非编码 RNA（lncRNA）由于其在癌症疾病中的潜在作用而几乎没有或没有蛋白质编码能力，因此引起了特别的关注。我们的研究旨在开发基于 lncRNA 的分类器和列线图，将 lncRNA 分类器和临床病理因素结合起来，以帮助提高头颈部鳞状细胞癌（HNSCC）患者复发预测的准确性。HNSCC lncRNA 分析数据和相应的临床病理信息从 TANRIC 数据库和 cBioPortal 下载。使用单变量 Cox 回归和最小绝对值收缩和选择算子（LASSO）分析，我们开发了 15 个与复发相关的 lncRNA 分类器。基于多变量 Cox 回归分析结果，构建了一个整合基因组和临床病理预测因子的列线图。通过校准曲线和一致性指数（C 指数）验证了包含列线图的预测准确性和判别能力，并通过 C 指数、接收者操作特征（ROC）分析与 TNM 分期系统进行比较。决策曲线分析（DCA）用于评估我们列线图的临床价值。结果，确定了 15 个与无复发生存（RFS）相关的 lncRNA，由建立的 15 个 lncRNA 组成的分类器可以有效地将患者分为高风险和低风险亚组。15-lncRNA 分类器预测 3 年和 5 年 RFS 的能力分别为 0.833 和 0.771。多变量分析中得出的预测复发的独立因素是阳性 LNs 的数量、切缘状态、突变计数和 lncRNA 分类器，这些因素都被嵌入到列线图中。复发概率的校准曲线表明，基于列线图的预测与实际观察结果吻合良好。列线图的 C 指数为 0.76（0.72-0.79），列线图预测 RFS 的 AUC 为 0.809，均明显高于传统的 TNM 分期和 15-lncRNA 分类器。决策曲线分析进一步表明，与 TNM 分期和 15-lncRNA 分类器相比，我们的列线图具有更大的净收益。结果得到了外部验证。总之，一个包含基因组和临床病理变量的 HNSCC 患者的直观综合列线图，与单独使用 TNM 分期相比，能更准确地预测复发概率，但在临床实践中使用之前，还需要更多的数据。
