Development and validation of a predictive model for immune-related genes in patients with tongue squamous cell carcinoma

Abstract The present study involved building a model of immune-related genes (IRGs) that can predict the survival outcomes of tongue squamous cell carcinoma (TSCC). Using the TCGA database, we collected the gene expression profiles of patients with TSCC and analyzed the differences in IRGs obtained from the ImmPort database. Subsequently, we constructed a predictive model. Transcription factors and differentially expressed IRGs can be used to construct TSCC regulatory network. CIBERSORT tool was used to analyze the relative proportion of 22 tumor-infiltrating immune cells in TSCC samples. Finally, a prognostic model is constructed. We established an IRG model formed by seven genes. The receiver operating characteristic value of the prognostic model based on IRGs is 0.739. After the analysis of the correlation between IRGs and clinical and pathological conditions, we found that Gast was related to grade, IRF9, LTB, and T stage. Among the 22 tumor-infiltrating immune cells, the resting natural killer (NK) cells were found to be related to the 5-year survival rate. This study constructed a prognostic model formed by seven IRGs and discussed the tumor-infiltrating immune cells, which are related to the survival outcome, reflecting the potential regulatory role of TSCC tumor immune microenvironment that could potentially promote individualized treatment.