Research Papers:

A six-mRNA signature model for the prognosis of head and neck squamous cell carcinoma

Wenna Guo, Xijia Chen, Liucun Zhu and Qiang Wang _

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Oncotarget. 2017; 8:94528-94538. https://doi.org/10.18632/oncotarget.21786

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Wenna Guo1, Xijia Chen2, Liucun Zhu2 and Qiang Wang1

1State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China

2School of Life Sciences, Shanghai University, Shanghai, China

Correspondence to:

Qiang Wang, email: [email protected]

Liucun Zhu, email: [email protected]

Keywords: biomarker, HNSCC, prognosis, risk-stratification, RNA-seq

Received: August 18, 2017     Accepted: September 22, 2017     Published: October 10, 2017


Head and neck squamous cell carcinoma (HNSCC), one of the most common cancers with high morbidity and mortality rates worldwide, has a poor prognosis. The transcriptome sequencing data of 500 patients with HNSCC in the TCGA dataset were assessed to find biomarkers associated with HNSCC prognosis so as to improve the prognosis of patients with HNSCC. The patients were divided into the training and testing sets. A model of six mRNAs (FRMD5, PCMT1, PDGFA, TMC8, YIPF4, ZNF324B) that could predict patient prognosis was identified in the training set using the Cox regression analysis. According to this model, the patients were divided into high-risk and low-risk groups. The Kaplan-Meier analysis showed that the high-risk group showed significantly shorter overall survival time compared with the low-risk group in both training and testing sets. The receiver operating characteristic analysis further confirmed high sensitivity and specificity for the model, which was more accurate compared with some known biomarkers in predicting HNSCC prognosis. Moreover, the model was applicable to patients of different ages, genders, clinical stages, tumor locations, smoking history, and human papillomavirus (HPV) status, as well as to microarray dataset. This model could be used as a novel biomarker for the prognosis of HNSCC and a significant tool for guiding the clinical treatment of HNSCC. The risk score acquired from the model might contribute to improving outcome prediction and management for patients with HNSCC, indicating its clinical significance.

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