A Genomic-Clinicopathologic Nomogram Predicts Survival for Patients with Laryngeal Squamous Cell Carcinoma

Abstract

Background: Long noncoding 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 lncRNA signature and a nomogram incorporating the genomic and clinicopathologic factors to improve the accuracy of survival prediction for laryngeal squamous cell carcinoma (LSCC).

Methods: A LSCC RNA-sequencing (RNA-seq) dataset 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-lncRNA 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 the TNM staging system by C-index and receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was conducted to evaluate the clinical value of our nomogram.

Results: 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 subgroups, with area under curves (AUC) of 0.89 (3-year OS) and 0.885 (5-year OS). Independent factors derived from multivariable analysis to predict survival were margin status, tumor status, and lncRNA signature, which were all assembled into the nomogram. The calibration curve for the survival probability showed that the predictions based on the nomogram coincided well with actual observations. The C-index of the nomogram was 0.82 (0.77-0.87), and the area under curve (AUC) of the nomogram in predicting overall survival (OS) was 0.938, both of which were significantly higher than the traditional TNM stage. Decision curve analysis further demonstrated that our nomogram had larger net benefit than TNM stage.

Conclusion: An inclusive nomogram for patients with LSCC, comprising genomic and clinicopathologic variables, generates more accurate estimations of the survival probability when compared with TNM stage alone, but more data are needed before the nomogram is used in clinical practice.

Figure 1

Thirteen lncRNAs selected by LASSO Cox regression analysis. (a) The two dotted vertical lines are drawn at the optimal values by the minimum criteria (left) and 1 − s.e. criteria (right). Details are provided in Materials and Methods. (b) LASSO coefficient profiles of the 31 lncRNAs. A vertical line is drawn at the optimal value by 1 − s.e. criteria and results in thirteen nonzero coefficients. Thirteen lncRNAs—AC007907.1, AC025419.1, AC078993.1, AC090241.2, AL158166.1, AL355974.2, AL596330.1, HOXB-AS4, KLHL6-AS1, LHX1-DT, LINC00528, LINC01436, and TTTY14—with coefficients 0.2102, 0.0045, 0.1377, -0.3675, -0.0652, 0.0180, 0.1208, 0.0969, 0.2227, 0.1541, -0.0647, -0.0750, and -0.1360, respectively, were selected in the LASSO Cox regression model.

Figure 2

Development of lncRNA signature for the prediction of survival in LSCC patients. (a and b) Distribution of lncRNA-based classifier risk score. (c) Time-independent ROC curves with AUC values to evaluate predictive efficacy of the lncRNA signature risk score. (d) The Kaplan-Meier estimates of the patients' survival status and time using the optimal lncRNA signature risk score cutoff which divided patients into low-risk and high-risk groups.

Figure 3

Functional annotation of the prognostic lncRNAs. Significantly enriched using the coexpressed mRNAs of the lncRNAs in GO analysis (a) and KEGG pathway analysis (b).

Figure 4

Nomogram for predicting 3-year and 5-year survival probability of LSCC after laryngectomy. To estimate risk, calculate points for each variable by drawing a straight line from the patient's variable value to the axis labeled “Points.” Sum all points and draw a straight line from the total point axis to the 3-year and 5-year survival axis.

Figure 5

ROC curves compare the prognostic accuracy of the nomogram with TNM staging or lncRNA signature in predicting survival probability (a). Decision curve analysis for the nomogram, TNM staging, and lncRNA signature in prediction of prognosis of patients (b).

Figure 6

ROC curve analyses for survival prediction in subgroups of patients with LSCC. (a) Advanced LSCC subgroup and (b) early LSCC subgroup.

Figure 7

The Kaplan-Meier estimates of patients' survival status and time using the optimal nomogram risk score cutoff which divided patients into low-risk and high-risk groups in subgroups of patients with LSCC. (a) Advanced LSCC subgroup and (b) early LSCC subgroup.