Data Mining Identifies Six Proteins that Can Act as Prognostic Markers for Head and Neck Squamous Cell Carcinoma

Abstract

Head and neck squamous cell carcinoma (HNSCC) is a malignant tumor of the upper aerodigestive tract affecting the oral cavity, lips, paranasal sinuses, larynx, and nasopharynx. Proteogenomics combines proteomics and genomics and employs mass spectrometry and high-throughput sequencing technologies to identify novel peptides. The aim of this study was to identify potential protein biomarkers for clinical treatment of HNSCC. To achieve this, we utilized two sets of data, one on proteins from The Cancer Proteome Atlas (TCPA) and the other on gene expression from The Cancer Genome Atlas (TCGA) database, to evaluate dysfunctional proteogenomics microenvironment. Univariate Cox regression analysis was performed to examine the relationship between protein signatures and prognosis. A total of 19 proteins were significantly associated with overall survival (OS) of patients, of which E2F transcription factor 1 (E2F1; HR = 4.557, 95% CI = 1.810 to 11.469) and enhancer of zeste homolog 2 (EZH2; HR = 0.430, 95% CI = 0.187 to 0.984) were the most differentially expressed between patients with longer and shorter OS, respectively. Furthermore, multivariate Cox regression analysis on six proteins (ERALPHA, HER3, BRAF, P27, RAPTOR, and E2F1) was performed to build the prognostic model. The receiver operating characteristic curves were used to determine whether the expression pattern of survival-related proteins could provide an early prediction of the occurrence of HNSCC. Herein, we found an AUC of 0.720. Based on an online database, we identified novel protein markers for the prognosis of HNSCC. The findings of the present study may provide new insights into the development of new and reliable tools for precise cancer intervention.