Identification of a gene expression signature predicting survival in oral cavity squamous cell carcinoma using Monte...

This study aims to identify a robust signature that performs well in predicting overall survival across tumor phenotypes and treatment strata, and validates the application of Monte Carlo cross validation (MCCV) as a means of identifying molecular signatures when utilizing small and highly heterogeneous datasets.

RNA sequence gene expression data for 264 patient tumors were acquired from The Cancer Genome Atlas (TCGA). 100 iterations of Monte Carlo cross validation were applied to differential expression and Cox model validation. The association between the gene signature risk score and overall survival was measured using Kaplan-Meier survival curves, univariate, and multivariable Cox regression analyses.

Pathway analysis findings indicate that ligand-gated ion channel pathways are the most significantly enriched with the genes in the aggregated signature. The aggregated signature described in this study is predictive of overall survival in oral cancer patients across demographic and treatment strata.

This study reinforces previous findings supporting the role of ion channel gating, interleukin, calcitonin receptor, and keratinization pathways in tumor progression and treatment response in oral cancer. These results strengthen the argument that differential expression of genes within these pathways reduces tumor susceptibility to treatment. Conducting differential gene expression (DGE) with Monte Carlo cross validation, as this study describes, offers a potential solution to decreasing the variability in DGE results across future studies that are reliant upon highly heterogeneous datasets. This improves the ability of studies reliant upon similarly structured datasets to reach results that are reproducible.