Integrated analysis of the functions of RNA binding proteins in clear cell renal cell carcinoma

Abstract Background: RNA binding proteins (RBPs) dysregulation is involved in the process es of various tumor. However, the roles of RBPs in clear cell renal cell carcinoma (ccRCC) remain poorly understand. Systematic exploration of the roles of RBPs in ccRCC may provide new insights for the treatments of ccRCC. Methods: Expression data of RBPs was obtained from The Cancer Genome Atlas database. The roles of RBPs in ccRCC were systematically investigated using consensus clustering methods. Differentially expressed RBPs between normal and tumor tissues were obtained. Protein-protein interaction (PPI) network was constructed using “STRING” software. The expression levels of hub genes were validated in The Human Protein Atlas (HPA) database and receiver operating characteristic (ROC) curves were used to evaluate diagnostic value . Univariate and Lasso Cox regression and Kaplan–Meier curves were used to screen the most useful prognostic genes. Multivariate Cox regression was performed to construct a risk score model. The efficiency of the model was evaluated using time-dependent ROC and Kaplan–Meier curves, and validated in E-MTAB-3267 set. Results: Two clusters were identified based on the expression similarity of RBPs, and the cluster 2 was closely correlated with the malignancy of ccRCC. Several oncogenic pathways, including epithelial mesenchymal transition, G2M checkpoint, KRAS signaling and IL6 JAK STAT3 signaling were enriched in cluster 2. In addition, we obtained 115 differently expressed RBPs in ccRCC, comprising 71 up-regulated and 44 down-regulated ones. Ten hub RBPs with good diagnostic value were obtained from PPI network and validated in HPA database. Ten RBPs were identified as survival-related genes and used to construct a risk score model. The model could be used to stratify patients with different prognosis. We found high-risk patients tended to be advanced stage, high grade, high pathological T staging and could be an independent risk factor for overall survival of ccRCC patients. Conclusions: We identified ten RBPs with diagnostic value, which might be the potential diagnostic biomarkers for ccRCC. A risk score model was established to stratify patients and could be used as a complementation for clinical factors to guide clinical practice in the future.