Abstract
Cumulative studies have shown that RNA binding proteins (RBPs) play an important role in numerous malignant tumors and are related to the occurrence and progression of tumors. However, the role of RBPs in kidney renal clear cell carcinoma (KIRC) is not fully understood. In this study, we first downloaded gene expression data and corresponding clinical information of KIRC from the Cancer Genome Atlas (TCGA) database, International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) database, respectively. A total of 137 differentially expressed RBPs (DERBPs) were then identified between normal and tumor tissue, including 38 downregulated and 99 upregulated RBPs. Nine RBPs (EIF4A1, RPL36A, EXOSC5, RPL28, RPL13, RPS19, RPS2, EEF1A2, and OASL) were served as prognostic genes and exploited to construct a prognostic model through survival analysis. Kaplan-Meier curves analysis showed that the low-risk group had a better survival outcome when compared with the high-risk group. The area under the curve (AUC) value of the prognostic model was 0.713 in the TCGA data set (training data set), 0.706 in the ICGC data set, and 0.687 in the GSE29609 data set, respectively, confirming a good prognostic model. The prognostic model was also identified as an independent prognostic factor for KIRC survival by performing cox regression analysis. In addition, we also built a nomogram relying on age and the prognostic model and internal validation in the TCGA data set. The clinical benefit of the prognostic model was revealed by decision curve analysis (DCA). Gene set enrichment analysis revealed several crucial pathways (ERBB signaling pathway, pathways in cancer, MTOR signaling pathway, WNT signaling pathway, and TGF BETA signaling pathway) that may explain the underlying mechanisms of KIRC. Furthermore, potential drugs for KIRC treatment were predicted by the Connectivity Map (Cmap) database based on DERBPs, including several important drugs, such as depudecin and vorinostat, that could reverse KIRC gene expression, which may provide reference for the treatment of KIRC. In summary, we developed and validated a robust nine-RBP signature for KIRC prognosis prediction. A nomogram with risk score and age can be applied to promote the individualized prediction of overall survival in patients with KIRC. Moreover, the two drugs depudecin and vorinostat may contribute to KIRC treatment.
Highlights
Renal cell carcinoma (RCC) is one of the most common cancers in people and mainly classified as three types: kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), and malignancies of the chromophobe
To systemically investigate the relationship between these nine RBPs and prognosis of KIRC, we developed a nine-RBP signature-based risk score based on their cox coefficient: Risk score = (0.005121079 ∗ EIF4A1) + (−0.065342266 ∗ RPL36A) + (−0.074842527 ∗ EXOSC5) + (−0.007007688 ∗ RPL28) + (0.003365894 ∗ RPL13) + (−0.000184204 ∗ RPS19) + (0.000510318 ∗ RPS2) + (0.017008893 ∗EEF1A2) + (0.118627759 ∗ OASL) We calculated the risk score for each patient based on the risk formula and ranked them according to the risk score
We constructed and validated a nine-RBP signature to predict KIRC prognosis based on the cox regression coefficient using the univariate cox regression analysis, robust likelihoodbased survival analysis, multivariate cox regression analysis, and ROC analysis
Summary
Renal cell carcinoma (RCC) is one of the most common cancers in people and mainly classified as three types: kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), and malignancies of the chromophobe. It has been reported that about 14,240 people died and 62,700 newly validated patients with kidney cancer were discovered in the United States in 2016 (Siegel et al, 2015). RCC can be mainly divided into three subtypes: KIRC, KIRP, and malignancies of the chromophobe (Fernandez-Pello et al, 2017; Foshat and Eyzaguirre, 2017). Some well-known biomarkers of KIRC, such as VHL/HIF, PI3K/Akt/mTOR, and Ras/Raf/MEK/ERK, have been identified, the underlying molecular mechanism of KIRC still remains uncertain (Elfiky et al, 2011; Colbert et al, 2015). It is necessary to reveal the underlying mechanism of KIRC to develop effective drugs or methods for its diagnosis and treatment
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
