Expression profile analysis identifies a two-gene signature for prediction of head and neck squamous cell carcinoma patient survival

Abstract

Aim: The aim of this study is to identify a gene prognostic signature for the head-and-neck squamous cell carcinoma (HNSCC). HNSCC is one of the most common malignancies worldwide; however, the molecular mechanisms underlying the malignancy are unclear.

Materials and methods: We analyzed the gene expression profiles of GSE2379, GSE53819, and GSE59102 derived from the gene expression omnibus, and the cancer genome atlas (TCGA) HNSC databases. The R software was used to identify the differentially expressed genes (DEGs) between HNSCC tissues and normal controls. Gene ontology, Kyoto Encyclopedia of Genes and Genomes pathway, protein-protein interactions network, and survival analyses of common DEGs were also performed.

Results: A total of 52 upregulated and 31 downregulated DEGs were identified. Functional analyses demonstrated that these DEGs were mainly enriched in extracellular matrix-receptor interaction, focal adhesion, tyrosine metabolism, and cytokine-cytokine receptor interaction. According to the survival analyses, PLAU and SERPINE1 could predict the overall survival of HNSCC patients from the TCGA cohort. Multivariable Cox regression analyses showed that the PLAU and SERPINE1 were independent prognostic factors for HNSCC patients. The prediction power of this two-gene signature was evaluated through receiver operating characteristic curve analysis and achieved a better prognostic value than PLAU (area under curve 0.613 [95% confidence interval 0.569-0.656] vs. 0.577 [0.533-0.621]; P = 0.008) or SERPINE1 (0.613 [0.569-0.656] vs. 0.586 [0.541-0.629]; P = 0.043) when considered alone.

Conclusions: The study has identified a set of novel genes and pathways that play significant roles in the carcinogenesis and progression of HNSCC. This two-gene signature may prove to be a useful therapeutic target for HNSCC.

Keywords: Gene ontology; head-and-neck squamous cell carcinoma; protein-protein interactions network; signature; survival analysis.