Development and Validation of a Three-Gene-Based Prognostic Model for Predicting the Overall Survival of Head and Neck Squamous Cell Carcinoma Through Bioinformatics Analysis.

Yiyuan Han,Xiaolin Cao,Qing He,Xuemei Wang

doi:10.3389/fgene.2021.721199

Abstract

Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancer worldwide and seriously threats public health safety. Despite the improvement of diagnostic and treatment methods, the overall survival for advanced patients has not improved yet. This study aimed to sort out prognosis-related molecular biomarkers for HNSCC and establish a prognostic model to stratify the risk hazards and predicate the prognosis for these patients, providing a theoretical basis for the formulation of individual treatment plans. We firstly identified differentially expressed genes (DEGs) between HNSCC tissues and normal tissues via joint analysis based on GEO databases. Then a total of 11 hub genes were selected for single-gene prognostic analysis to identify the prognostic genes. Later, the clinical information and transcription information of HNSCC were downloaded from the TCGA database. With the application of least absolute shrinkage and selection operator (LASSO) algorithm analyses for the prognostic genes on the TCGA cohort, a prognostic model consisting of three genes (COL4A1, PLAU and ITGA5) was successfully established and the survival analyses showed that the prognostic model possessed a robust performance in the overall survival prediction. Afterward, the univariate and multivariate regression analysis indicated that the prognostic model could be an independent prognostic factor. Finally, the predicative efficiency of this model was well confirmed in an independent external HNSCC cohort.

Highlights

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide with increasing incidence and unpredictable prognosis (Ferlay et al, 2019)
GSE65858 (Wichmann et al, 2015) which contains a total of 270 tumor samples was selected to verify the robustness of the prognostic model based on the following criteria: 1) Number of tumor samples ≥200; 2) The datasets contain HNSCC samples arising from different primary sites including the oral cavity, pharynx, larynx, etc.; 3) The patients in the datasets have not received radiotherapy or chemotherapy; 4) Specific gene-knockout samples should not be contained in the datasets; 5) The clinical data is complete
Based on the criteria of adjusted p-value1, there were 282 upregulated genes and 202 downregulated genes identified in GSE107591 dataset, while 1,045 upregulated genes and 752 downregulated genes were identified from GSE29330 dataset

Summary

Introduction

HNSCC is the sixth most common cancer worldwide with increasing incidence and unpredictable prognosis (Ferlay et al, 2019). The initiation and development of HNSCC involve complex molecular changes, and environment exposure, viral infection and unhealthy lifestyle are the common risk factors (Johnson et al, 2020). The Prognostic Model for HNSCC methods, the overall survival for advanced cancers remains dismal (Price and Cohen, 2012). Elucidating the molecular mechanisms related to the tumorigenesis of HNSCC is of great importance to predicate and improve the prognosis of patients. The TNM staging system is one of the most extensively used indicator for monitoring HNSCC progression, it is very limited to further distinguish the clinical outcome of patients at the same TNM stage (Huang et al, 2019; Zhao and Cui, 2019). It is critical to find a novel and reliable prognostic modules model for successful clinical managements and personalized medicine

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