Co-expression Network Identification and Clinical Prognostic Evaluation of Hub Genes in Head and Neck Squamous Cell Carcinoma

Abstract

Background: According to statistics, even with active treatment, the recurrence rate of HNSCC is at 40%-50%. Head and neck cancer remains a challenge for otolaryngologists. Therefore, the identification of new biomarkers is an urgent need for the diagnosis, treatment, and prognosis of malignant tumors of the head and neck. Methods: In this study, transcriptome data from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database were used to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was performed to identify gene modules and hub genes related to head and neck squamous cell carcinoma (HNSCC). Protei-Protei interaction(PPI) network and Cytoscape software were used to analyze the protein interaction network. HNSCC clinical data from the TCGA and Gene Expression Profile Interactive Analysis 2 databases were used to analyze the survival rate of hub genes, and the correlation between hub genes and tumor stage was further analyzed.Results: A total of 2836 and 570 DEGs were identified from the TCGA expression data and GEO gene chip datasets, respectively. We found that the green module had the highest correlation with HNSCC. A total of 15 hub genes were also identified. In the Human Protein Atlas database, we found that thioredoxin reductase 1 (TXNRD1) was overexpressed in HNSCC tumors compared with normal tissues at the transcriptional level. Survival analysis also suggested that TXNRD1 was a poor prognostic factor for HNSCC.Conclusion: Our results indicate that TXNRD1 is very likely to be identified as a potential biomarker and target for HNSCC. However, further research is required to fully reveal its role in HNSCC pathogenesis as well as its value as a prognostic biomarker.