Construction and Validation of an RNA-Binding Protein-Related Bladder Cancer Prognostic Model Based on Bioinformatics

Abstract

Objective: This study aimed to construct a bladder cancer prognostic model using bioinformatics to predict the survival of bladder cancer patients. Methods: RNA sequences and corresponding clinical data were downloaded from The Cancer Genome Atlas (TCGA) database, and the differentially expressed RNA-binding proteins (RBPs) were screened for analysis using the limma software package. Then, GO enrichment analysis and KEGG pathway analysis were performed on these differentially expressed RNA-binding proteins, and a PPI network was constructed. Finally, a risk model was constructed based on the screened central RBP, and a Kaplan-Meier survival curve was drawn to evaluate the prognostic value of central RBP and predict the prognosis of bladder Cancer(BLCA) patients with this model. Finally, the human protein atlas (HPA) online database (http://www.proteinatlas.org/) was used to further detect the differential expression of central hub RBP at the protein level between tumor tissue and normal tissue. Results: The bladder cancer prognostic model constructed with these six central RBPs had good sensitivity and specificity in predicting the prognosis of bladder cancer patients. Conclusion: This study explored the genes and regulatory networks of bladder cancer prognosis-related RNA-binding protein and bladder cancer, and constructed a bladder cancer prognosis model, which provides a theoretical basis for the development of new bladder cancer prognosis biomarkers in the future.