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  • Title: Identification for Exploring Underlying Pathogenesis and Therapy Strategy of Oral Squamous Cell Carcinoma by Bioinformatics Analysis.
    Author: Xu Z, Jiang P, He S.
    Journal: Med Sci Monit; 2019 Dec 03; 25():9216-9226. PubMed ID: 31794546.
    Abstract:
    BACKGROUND Oral squamous cell carcinoma (OSCC), one of the most common cavity-associated cancers, has a high incidence and worldwide mortality. However, the cause and underlying molecular mechanisms of OSCC remain unclear. MATERIAL AND METHODS Three microarray datasets (GSE23558, GSE34105, and GSE74530) from the Gene Expression Omnibus (GEO) database were downloaded and then integrated to gain differentially expressed genes (DEGs). We performed Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs in order to elucidate DEGs' biological roles. Protein-protein interaction (PPI) networks were established in order to identify hub genes. To validate the gene markers for OSCC, the data of TCGA OSCC were also assessed. RESULTS Together, 651 DEGs containing 288 upregulated genes and 363 downregulated genes were screened out, which could completely distinguish between OSCC and normal control tissues by principal component analysis (PCA). The GO analysis indicated the DEGs were enriched in chemokine activity in the biological process group. The molecular functions of DEGs included growth factor activity. The molecular functions included oxidoreductase activity. The main DEG-associated cellular components included extracellular exosome. The KEGG pathway analysis indicated the DEGs were mainly participated in the cytokine-cytokine receptor interaction, metabolism of xenobiotics by cytochrome P450 and glutathione metabolism signal pathway. The co-expression network identified core genes from the PPI network. Additionally, Kaplan-Meier survival analysis showed that CSF2 and EGF genes were significantly correlated with OSCC patients' overall survival. CONCLUSIONS Our study using an integrated bioinformatics analysis might provide valuable information for exploring potential new molecular biomarkers and therapeutic targets for OSCC.
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