Identification and analysis of genes associated with head and neck squamous cell carcinoma by integrated bioinformatics methods

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancers worldwide, exhibiting high morbidity and mortality. The prognosis of HNSCC patients has remained poor, though considerable efforts have been made to improve the treatment of this cancer. Therefore, identifying significant differentially expressed genes (DEGs) involved in HNSCC progression and exploiting them as novel biomarkers or potential therapeutic targets for HNSCC is highly valuable. METHODS: Overlapping differentially expressed genes (DEGs) were screened out from three independent gene expression omnibus (GEO) datasets and subjected to GO and kyoto encyclopedia of genes and genomes pathway enrichment analyses. The protein–protein interactions network of DEGs was constructed in the STRING database, and the top ten hub genes were selected using cytoHubba. The relative expression of hub genes was detected in GEPIA, Oncomine, and human protein atlas (HPA) databases. Furthermore, the relationship of hub genes with the overall survival and disease‐free survival in HNSCC patients was investigated using the cancer genome atlas data. RESULTS: The top ten hub genes (SPP1, POSTN, COL1A2, FN1, IGFBP3, APP, MMP3, MMP13, CXCL8, and CXCL12) could be utilized as potential diagnostic indicators for HNSCC. The relative levels of FN1, APP, SPP1, and POSTN could be associated with the prognosis of HNSCC patients. The mRNA expression of APP and COL1A2 was validated in HNSCC samples. CONCLUSION: This study identified effective and reliable molecular biomarkers for diagnosis and prognosis by integrated bioinformatics analysis, suggesting novel and essential therapeutic targets for HNSCC.