Objectives: This study was designed to identify a messenger RNA (mRNA) expression signature to predict survival in patients with oral squamous cell carcinoma (OSCC). Methods: mRNA expression profiles were integrated with clinical data from 280 samples, including 19 normal tissues and 261 OSCC tissues in The Cancer Genome Atlas. We identified differentially expressed mRNAs (DEmRNAs) between the OSCC and normal tissue samples and developed a novel mRNA-focused expression signature using a Cox regression analysis and other bioinformatic methods. The prognostic value of this signature was evaluated by Kaplan-Meier analysis, multivariable COX regression, and receiver operating characteristic (ROC) curve analysis. Protein-protein interaction (PPI) network, gene ontology, and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed to predict the function of the DEmRNAs. Signature-related mRNAs were analyzed by gene set enrichment analyses (GSEA) and validated by quantitative real-time polymerase chain reaction (qRT-PCR) in 20 paired OSCC and adjacent healthy tissues. Results: We identified a novel 5-mRNA expression signature (HOXA1, CELSR3, HIST1H3J, ZFP42, and ASCL4) that could predict patient outcomes in OSCC. The risk score based on the signature was able to separate OSCC patients into high- and low-risk groups that showed significantly different overall survival (p < 0.001, log-rank test). The signature was further validated as an effective independent prognostic predictor of OSCC by multivariate Cox regression analysis (hazard ratio = 3.747, confidence interval: 2.279-5.677, p < 0.001) and ROC curve of the third year (area under the curve = 0.733). Functional analysis demonstrated that the key hub genes in the PPI network were mainly enriched in cell division, cell proliferation, and the p53 signaling pathway. GSEA results showed that the 5 mRNAs were significantly enriched in mismatch repair, DNA replication, and the NOTCH signaling pathway. Finally, qRT-PCR results showed that the 5 mRNAs were upregulated in OSCC tissue in agreement with the predictions from our bioinformatics analysis. Conclusions: We identified a novel 5-mRNA signature that could predict the survival of patients with OSCC and may be a promising biomarker for personalized cancer treatments.
Keywords: bioinformatics analysis; mRNA signature; oral squamous cell carcinoma.