Identification of E2F-Related Subtypes, the Development of a Prognosis Model, and Characterization of Tumor Microenvironment Infiltration in Oral Squamous Cell Carcinoma

Abstract

Objective: To characterize oral squamous cell carcinoma (OSCC) subtypes and prognostic models with early region 2 binding factor (E2F) families. Methods: Expression and clinical information of OSCC and normal samples were downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases as testing and validation sets, respectively. Based on E2Fs, unsupervised cluster analysis was used to classify the different subtypes of OSCC. We then screened for genes that were differentially expressed (DEGs) between tumor and normal tissues and used weighted gene co-expression network analysis (WGCNA) to screen for genes in the modules most significantly associated with prognosis. DEGs that were significantly associated with prognosis were used to construct a risk model. Finally, a nomogram combining clinically independent factors was constructed. Results: OSCC samples were divided into two subtypes. There was a significant difference in prognosis between the two subtypes of patients (p < 0.05). Twenty-five DEGs that were significantly associated with prognosis were screened from the 153 subtype-related genes. Subsequently, five genes were used to construct a risk model using the least absolute shrinkage and selection operator (LASSO) method. In both the TCGA and GEO datasets, it was found that the prognosis of patients in the high-risk group was significantly worse than that in the low-risk group (p < 0.05). Age, N stage, and risk group were independent prognostic factors for OSCC and were used to construct the nomogram, which had a good prognostic performance. Conclusions: The risk model based on E2F-related subtypes and DEGs showed good predictive performance for the prognosis of patients with OSCC. Nomograms constructed using independent prognostic factors, including risk score, may play an important role in personalizing prognosis and treatment in the future.