Identification of novel drug candidates for treating tongue squamous cell carcinoma using computational approaches

Abstract BackgroundTongue squamous cell carcinoma (TSCC) is one of the most common oral squamous cell carcinoma (OSCC) with a high occurrence and a poor prognosis, yet its molecular mechanisms are largely unknown and novel drug candidates are needed. The purpose of this study was to construct gene co-expression networks to identify hub proteins significantly associated with tumor grades and the overall survival (OS) of TSCC patients and provide potential drug candidates. MethodsThe mRNA-sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) dataset. Weighted gene co-expression network analysis (WGCNA) was used for identifying the co-expression module related to the tumor grade of TSCC. The hub proteins were selected by the interaction number with other proteins, and their correlations with prognosis and tumor grades were calculated. Virtual screening of compounds by the hub protein structures was used to identify the drug candidates. ResultsWGCNA identified ten co-expression modules, in which the brown module consisted of 163 genes was most significantly correlated with the tumor grade. Six hub genes/proteins (BUB1, CCNB2, CDC6, CDC20, CDK1, and MCM2) tended to be in the central hub of the network. And higher expression levels of these hub genes were associated with tumor grades and worse overall survival. Three compounds, targeting hub proteins, demonstrated high binding affinities, favorable pharmacologic properties, and low toxicity. Conclusion The gene co-expression network-based study could provide additional insight into tumorigenesis and progression of TSCC, and our study might provide novel drug candidates.