Search

Head and neck squamous cell carcinoma (HNSCC) is one of the most common malignant cancers in the world. In advanced stages, the mortality rate is high. Stratification of HNSCC patients by molecular characteristics allows optimization of clinical management of these patients. Here, we explored the GEO and PRIDE Databases to identify putative prognostic biomarkers for HNSCC. Thus, an integrated transcriptome analysis was performed to identify the major differentially expressed genes in HNSCC tissues (using GSE12452, GSE13597, GSE31056, GSE6631 and GSE3524 GEO Datasets) in combination with a proteomic analysis to identify the overrepresented proteins in saliva samples from HNSCC patients (using PRIDE PXD012436 dataset). The panel of identified biomarkers was characterized using web tools, namely UALCAN, ToPP and PINAv3.0. From the combinatorial analysis of these Omics data, the salivary biomarkers with the greatest potential for clinical application for prognostic stratification were identified: ADH7, MMP9, and S100A14. Overall, this Multi-Omics approach provides comprehensive information on the potential value of combining ADH7, MMP9, and S100A14 as prognostic biomarker panel for risk stratification. Future studies should validate this panel of biomarkers in saliva samples from a large cohort of patients using targeted approaches, envisioning its translation into the clinical setting.

Head and Neck Squamous Cell Carcinoma (HNSCC) is a malignant cancer with poor prognosis. Currently, the prognosis of HNSCC is determined by clinical and histopathological criteria. This work focused on identifying a panel of genes that have the potential to be used for prognosis of HNSCC and to improve patient stratification for treatment. To this end, a bibliometric analysis (VosViewer) was applied to identify candidate genes that were further characterized by applying several bioinformatics tools (UALCAN, ToPP). The prognostic potential of the genes of interest was evaluated using the univariate and the multivariate Cox proportional regression models and the transcriptional expression analysis among HNSCC and normal tissues. In HNSCC, the transcriptional levels of candidate genes, were analyzed in HPV-driven HNSCC, HPV-non-driven HNSCC, TP53-mutant HNSCC and TP53-nonmutant HNSCC for selecting the best set of genes for discrimination of HNSCC based on both HPV status and TP53 mutational status. These analyses revealed a signature based on four genes with greater HNSCC prognostic potential: CDKN2A, TGFB1, CD44 and MMP9, being p16 the sole biomarker currently tested. In the future, a molecular signature could facilitate the stratification of patients into high- and low-risk groups as well the wiser adjustment of therapies to each individual response allowing a personalized treatment.