A genomic-clinicopathologic nomogram predict survival for patients with laryngeal squamous cell carcinoma

Abstract

Long non-coding RNAs (lncRNAs), which have little or no ability to encode proteins, have attracted special attention due to their potential role in cancer disease. We aimed to establish a lncRNAs signature and a nomogram incorporating the genomic and clinicopathologic factors to improve the accuracy of survival prediction for laryngeal squamous cell carcinoma (LSCC). LSCC RNA sequencing (RNA-seq) data set and the matched clinicopathologic information were downloaded from the Cancer Genome Atlas (TCGA). Using univariable Cox regression and Least absolute shrinkage and selection operator (LASSO) analysis, we developed a thirteen lncRNAs signature related to prognosis. 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 assess clinical value of our nomogram. Consequently, thirteen overall survival (OS) -related lncRNAs were identified, and the signature consisting of the selected thirteen lncRNAs could effectively divide patients into high-risk and low-risk subgroup, with the area under curve (AUC) of 0.89 (3-year OS) and AUC of 0.885(5-year OS). Independent factors derived from multivariable analysis to predict survival were margin status, tumor status and lncRNAs signature, which were all assembled into the nomogram. The calibration curve for the survival probability showed that the predictions based on the nomogram were in good coincide with actual observations. The C-index of the nomogram was 0.82 (0.77-0.87), and the area under curve (AUC) of nomogram in predicting overall survival (OS) was 0.938, which were significantly higher than traditional TNM stage. Decision curve analysis further demonstrated that our nomogram had larger net benefit than TNM stage. In summary, an inclusive nomogram for patients with LSCC, comprising genomic and clinicopathologic variables, generates more accurate estimations of the survival probability when compared TNM stage alone, but more additional data remains needed before being used in clinical practice.