Survival-Based Biomarker Module Identification Associated with Oral Squamous Cell Carcinoma (OSCC).

Abstract

Simple SummaryIn this study, four OSCC-specific hub genes were identified using high-throughput RNA-Seq data from TCGA cohort. The significant genes within tumor and normal samples were used for weighted PPI network construction based on survival of patients along with their expression profiles. The analysis revealed the most significant module in the training and test datasets. The genes from this module were used for pathway enrichment analysis followed by hub gene selection. These novel biomarkers might have clinical utility for diagnosis and prognosis prediction in OSCC, providing diagnosis at a very early stage. Moreover, a combination of all these biomarkers might distinguish the OSCC patients with low risk and high risk for cancer progression and recurrence, which will provide useful guidance for personalized and precision therapy. However, the results in the present study were obtained by integrative theoretical analysis, and the findings remain to be confirmed by further experimental validations.Head and neck squamous cell carcinoma (HNSC) is one of the most common malignant tumors worldwide with a high rate of morbidity and mortality, with 90% of predilections occurring for oral squamous cell carcinoma (OSCC). Cancers of the mouth account for 40% of head and neck cancers, including squamous cell carcinomas of the tongue, floor of the mouth, buccal mucosa, lips, hard and soft palate, and gingival. OSCC is the most devastating and commonly occurring oral malignancy, with a mortality rate of 500,000 deaths per year. This has imposed a strong necessity to discover driver genes responsible for its progression and malignancy. In the present study we filtered oral squamous cell carcinoma tissue samples from TCGA-HNSC cohort, which we followed by constructing a weighted PPI network based on the survival of patients and the expression profiles of samples collected from them. We found a total of 46 modules, with 18 modules having more than five edges. The KM and ME analyses revealed a single module (with 12 genes) as significant in the training and test datasets. The genes from this significant module were subjected to pathway enrichment analysis for identification of significant pathways and involved genes. Finally, the overlapping genes between gene sets ranked on the basis of weighted PPI module centralities (i.e., degree and eigenvector), significant pathway genes, and DEGs from a microarray OSCC dataset were considered as OSCC-specific hub genes. These hub genes were clinically validated using the IHC images available from the Human Protein Atlas (HPA) database.