Transcriptomics data mining to uncover signature genes in head and neck squamous cell carcinoma: a bioinformatics...

Due to its heterogeneous nature, head and neck squamous cell carcinoma (HNSC) had the worst prognosis. Hence, there is an urgent need to develop novel diagnostic and prognostic models for effective disease management. A multi-layer dry-lab and wet-lab methodologies were adopted in the present study to identify novel diagnostic and prognostic biomarkers of HNSC. Initially, the GSE6631 gene microarray HNSC dataset was retrieved from the Gene Expression Omnibus (GEO) database. The R language-based "limma" package was employed to identify differentially expressed genes (DEGs) between HNSC and control samples. The Cytohubba plug-in software was used to identify the top four hub genes based on the degree score method. The Cancer Genome Atlas (TCGA) datasets, Gene Expression Omnibus (GEO) datasets, clinical HNSC tissue samples, HNSC cell line (FaDu), and normal cell line (HOK) were used to validate the expressions of hub genes. Moreover, additional bioinformatics analyses were performed to further evaluate the mechanisms of hub genes in the development of HNSC. In total, 1372 reliable DEGs were screened from the GSE6631 dataset. Out of these DEGs, only based on the four up-regulated hub genes, including UBE2C (Ubiquitin-conjugating enzyme E2C), BUB1B (BUB1 Mitotic Checkpoint Serine/Threonine Kinase B), MCM4 (Minichromosome Maintenance Complex Component 4), and KIF23 (Kinesin family member 23), we developed and validated a diagnostic and prognostic model for HNSC patients. Moreover, some interesting correlations observed between hub gene expression and infiltration level of immune cells may also improve our understanding of HNSC immunotherapy. In conclusion, we developed a novel diagnostic and prognostic model consisting of the UBE2C, BUB1B, MCM4, and KIF23 genes for HNSC patients. However, the efficiency of this model needs to be verified through more experimental studies.