The exploration of new biomarkers for oral cancer through the ceRNA network and immune microenvironment analysis

Abstract

The competitive endogenous RNA (ceRNA) and tumor-penetrating immune cells may be related to the prognosis of oral cancer. However, few studies have focused on the correlation between ceRNAs and immune cells. Thus, we developed a method based on a ceRNA network and tumor-infiltrating immune cells to elucidate the molecular pathways that may predict prognosis in patients with oral cancer. Download RNAseq expression data of oral cancer and control samples from the Cancer Genome Atlas (TCGA), obtain differentially expressed genes and establish a ceRNA network. The cox analysis and lasso regression analysis were used to screen key RNAs to establish a prognostic risk assessment model, and draw a 1.3.5-year forecast nomogram. Then the CIBERSORT algorithm was used to screen important tumor immune infiltrating cells associated with oral cancer. Another prognostic predictive model related to immune cells was established. Finally, co-expression analysis was applied to explore the relationship between key genes in the ceRNA network and important immune cells. Multiple external data sets are used to test the expression of key biomarkers. We constructed prognostic risk models of ceRNA and immune cells, which included 9 differentially expressed mRNAs and 2 types of immune cells. It was discovered from the co-expression analysis that a pair of important biomarkers were associated with the prognosis of oral cancer. T cells regulatory and CGNL1 (R = 0.39, P < .001) showed a significant positive correlation. External data set validation also supports this result. In this study, we found that some crucial ceRNAs (GGCT, TRPS1, CGNL1, HENMT1, LCE3A, S100A8, ZNF347, TMEM144, TMEM192) and immune cells (T cells regulatory and Eosinophils) may be related to the prognosis of oral cancer.