Abstract
Background:
The cause and underlying molecular mechanisms of head and neck squamous cell carcinoma (HNSCC) are unclear. Our study aims to identify the key genes associated with HNSCC and reveal potential biomarkers.
Methods:
In this study, the expression profile dataset GSE83519 of the Gene Expression Omnibus database and the RNA sequencing dataset of HNSCC of The Cancer Genome Atlas were included for analysis. Sixteen differentially expressed genes were screened from these two datasets using R software. Gene Expression Profiling Interactive Analysis 2 (GEPIA2) was then adopted for survival analysis, and finally, three key genes related to the overall survival of HNSCC patients were identified. Furthermore, we verified these three genes using the Oncomine database and from real-time PCR and immunohistochemistry results from HNSCC tissues.
Results:
The expression data of 44 samples from GSE83519 and 545 samples from TCGA-HNSC were collected. Using bioinformatics, the two databases were integrated, and 16 DEGs were screened out. Gene Ontology (GO) enrichment analysis showed that the biological functions of DEGs focused primarily on the apical plasma membrane and regulation of anoikis. Kyoto Encyclopedia of Genes and Genomes (KEGG) signalling pathway analysis showed that these DEGs were mainly involved in drug metabolism-cytochrome P450 and serotonergic synapses. Survival analysis identified three key genes, CEACAM5, CEACAM6 and CLCA4, that were closely related to HNSCC prognosis. The Oncomine database, qRT-PCR and IHC verified that all 3 key genes were downregulated in most HNSCC tissues compared to adjacent normal tissues.
Conclusions:
This study indicates that integrated bioinformatics analyses play an important role in screening for differentially expressed genes and pathways in HNSCC, helping us better understand the biomarkers and molecular mechanism of HNSCC.
Keywords:
DEG (differentially expressed gene) analysis; GEO database; Head and neck squamous cell carcinoma (HNSCC); Integrated bioinformatics; The Cancer Genome Atlas (TCGA).