Development and validation of a prognostic model for kidney renal clear cell carcinoma based on RNA binding protein expression

Abstract

Dysregulated expression of RNA-binding proteins (RBPs) is strongly associated with the development and progression of multiple tumors. However, little is known about the role of RBPs in kidney renal clear cell carcinoma (KIRC). In this study, we examined RBP expression profiles using The Cancer Genome Atlas database and identified 133 RBPs that were differentially expressed in KIRC and non-tumor tissues. We then systematically analyzed the potential biological functions of these RBPs and established PPIs. Based on Lasso regression and Cox survival analyses, we constructed a risk model that could independently and accurately predict prognosis based on seven RBPs (NOL12, PABPC1L, RNASE2, RPL22L1, RBM47, OASL, and YBX3). Survival times were shorter in patients with high risk scores for cohorts stratified by different characteristics. Gene set enrichment analysis was also performed to further understand functional differences between high- and low-risk groups. Finally, we developed a clinical nomogram with a concordance index of 0.792 for estimating 3- and 5-year survival probabilities. Our results demonstrate that this risk model could potentially improve individualized diagnostic and therapeutic strategies.