Identification of Potential Prognostic Biomarkers for Tongue Squamous Cell Carcinoma

Abstract

Background: Tongue squamous cell carcinoma (TSCC) is one of the most common types of oral cancer and has a poor prognosis owing to a limited understanding of its pathogenetic mechanisms. The purpose of this study was to explore and identify potential biomarkers in TSCC by integrated bioinformatics analysis.Methods: The RNA sequencing data, methylation data, and clinical characteristics of TSCC patients were downloaded from The Cancer Genome Atlas (TCGA), and then differentially expressed RNAs (DERNAs), including differentially expressed long noncoding RNAs (DElncRNAs) and differentially expressed messenger RNAs (DEmRNAs), were identified in TSCC by bioinformatics analysis. Subsequently, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Hallmark pathway analyses were used to analyze the DERNAs. Univariate and multivariate Cox regression analyses were used to develop four-lncRNA and two-mRNA signatures and predict survival in TSCC patients. We established a risk model to predict the overall survival (OS) of TSCC patients based on the DERNAs with Kaplan–Meier analysis and the log-rank p test. Furthermore, weighted gene coexpression network analysis (WGCNA) was performed in Cytoscape, and a protein-protein interaction (PPI) network was established in the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database.Results: A total of 2,006 DEmRNAs and 1,001 DElncRNAs were found to be dysregulated in TSCC. A total of 417 DERNAs were used to construct the coexpression network, and the PPI network included 103 DEmRNAs. Univariate regression analysis of the DERNAs revealed 51 DElncRNAs and 90 DEmRNAs that were associated with OS in TSCC patients. Multivariate Cox regression analysis demonstrated that four of those lncRNAs (MGC32805, RP1-35C21.2, RP11-108K3.1, and RP11-109M17.2) and two mRNAs (CA9, GTSF1L) had significant prognostic value, and their cumulative risk score indicated that these four-lncRNA and two-mRNA signatures independently predicted OS in TSCC patients. Additionally, there was a positive correlation between the expression and methylation level of RP11-108K3.1, the OS significantly negatively correlated with hypermethylation and low expression of GTSF1L along with hypomethylation and high expression of CA9.Conclusions: The current findings provide novel insights into the molecular mechanisms of TSCC and identify four lncRNAs and two mRNAs that are potential biomarkers that may be independent prognostic signatures for TSCC diagnosis and treatment.