Research Paper Volume 12, Issue 24 pp 25356—25372
Development and validation of a prognostic model for kidney renal clear cell carcinoma based on RNA binding protein expression
- 1 Department of Urology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, Shandong, China
- 2 Department of Urology, Yiyuan County People's Hospital, Zibo 256100, Shandong, China
- 3 Department of Urology, Xintai People's Hospital, Xintai 271200, Shandong, China
- 4 Department of Central Sterile Supply, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, Shandong, China
- 5 Department of Rehabilitation Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, Shandong, China
- 6 Department of Urology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China
Received: June 11, 2020 Accepted: September 20, 2020 Published: November 20, 2020https://doi.org/10.18632/aging.104137
How to Cite
Copyright: © 2020 Xiang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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.
AJCC: American Joint Committee on Cancer; AUC: area under the curve; BP: biological processes; CC: cellular component; C-index: concordance index; DCA: decision curve analysis; DFS: disease free survival; GO: Gene Ontology; GSEA: Gene Set Enrichment Analysis; HR: hazard ratio; KEGG: Kyoto Encyclopedia of Genes and Genomes; KIRC: kidney renal clear cell carcinoma; LASSO: least absolute shrinkage and selection operator; MF: molecular function; OS: overall survival; PPI: protein-protein interaction; RBPs: RNA-binding proteins; RCC: renal cell carcinoma; ROC: receiver operating characteristic; TCGA: The Cancer Genome Atlas.