An enhancer RNA-based risk model for prediction of bladder cancer prognosis

BACKGROUND: Bladder cancer patients have a high recurrence and poor survival rates worldwide. Early diagnosis and intervention are the cornerstones for favorable prognosis. However, commonly used predictive tools cannot meet clinical needs because of their insufficient accuracy. METHODS: We have developed an enhancer RNA (eRNA)-based signature to improve the prediction for bladder cancer prognosis. First, we analyzed differentially expressed eRNAs in gene expression profiles and clinical data for bladder cancer from The Cancer Genome Atlas database. Then, we constructed a risk model for prognosis of bladder cancer patients, and analyzed the correlation between this model and tumor microenvironment (TME). Finally, regulatory network of downstream genes of eRNA in the model was constructed by WGCNA and enrichment analysis, then Real-time quantitative PCR verified the differentiation of related genes between tumor and adjacent tissue. RESULTS: We first constructed a risk model composed of eight eRNAs, and found the risk model could be an independent risk factor to predict the prognosis of bladder cancer. Then, the log-rank test and time-dependent ROC curve analysis shown the model has a favorable ability to predict prognosis. The eight risk eRNAs may participate in disease progression by regulating cell adhesion and invasion, and up-regulating immune checkpoints to suppress the immunity in TME. mRNA level change in related genes further validated regulatory roles of eRNAs in bladder cancer. In summary, we constructed an eRNA-based risk model and confirmed that the model could predict the prognosis of bladder cancer patients.