An integrated nomogram combining lncRNAs classifier and clinicopathologic factors to predict the recurrence of head and neck squamous cell carcinoma

Abstract

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.

Distinguishable Prognostic miRNA Signatures of Head and Neck Squamous Cell Cancer With or Without HPV Infection

“…Personalized treatment on the basis of HPV status with better efficacy and minimal side effects is on the horizon. Compared with previously established signatures irrespective of HPV status ( 21 – 25 ), the current study might be more accurate in predicting prognosis of HNSCC patients and, more importantly, better describe the potential molecular mechanisms by dividing samples into HPV+ group and HPV− group. According to the results, we speculated that HPV+ HNSCC patients with low signature score might have better immunity against the tumors and enhance the sensitivity of therapeutic interventions leading to improved prognosis, while HPV− HNSCC patients with high signature score acquired resistance to therapeutic approaches as well as dysregulation of cell metabolism responsible for poor prognosis.…”

“…Although this vital distinction mainly referred to oropharyngeal tumors within HNSCC, as this was the best-studied entity, HPV was also involved in 23.5% of oral cancer and 24% of laryngeal cancer cases ( 20 ). However, most of the identified prognostic signatures so far were still performed using total HNSCC samples regardless of HPV status ( 21 – 25 ). When HPV status was considered, most publications mainly focused on identifying differences in miRNA expression levels between HPV+ and HPV−, and HPV-associated oncogenic miRNA panel has potential utility in diagnosis and disease stratification of HNSCC ( 26 – 29 ).…”

Lymphoepithelial carcinoma of the head and neck: a SEER analysis of prognostic factors for survival

“…Nomograms have been used in clinical prediction research. 31 – 33 In this study, we established nomograms to predict 1-, 3-, and 5-year OS and CSS for patients with nasopharyngeal LEC. The nomograms were validated to determine the degree of model fit and the applicability of prediction.…”

Beyond MicroRNAs: Emerging Role of Other Non-Coding RNAs in HPV-Driven Cancers

“…These findings might explain why HPV-positive HNSCC are more sensitive to radiotherapy. By analyzing lncRNAs profiling data and the corresponding clinic-pathologic variables of 371 HNSCC patients from TANRIC and cBioPortal, Cui et al [132] defined a signature of 15 lncRNAs with prognostic significance for recurrence-free survival. Importantly, when HNSCC patients were stratified according to their HPV status, the 15 lncRNAs signature remained a clinically and statistically significant prognostic model.…”