Transcriptome Analyses Identify an RNA Binding Protein Related Prognostic Model for Clear Cell Renal Cell Carcinoma

Yue Wu; Xian Wei; Huan Feng; Bintao Hu; Bo Liu; Yang Luan; Yajun Ruan; Xiaming Liu; Zhuo Liu; Shaogang Wang; Jihong Liu; Tao Wang

doi:10.3389/fgene.2020.617872

Transcriptome Analyses Identify an RNA Binding Protein Related Prognostic Model for Clear Cell Renal Cell Carcinoma

Front Genet. 2021 Jan 7:11:617872. doi: 10.3389/fgene.2020.617872. eCollection 2020.

Authors

Yue Wu^{1

2}, Xian Wei^{1

2}, Huan Feng^{1

2}, Bintao Hu^{1

2}, Bo Liu³, Yang Luan^{1

2}, Yajun Ruan^{1

2}, Xiaming Liu^{1

2}, Zhuo Liu^{1

2}, Shaogang Wang^{1

2}, Jihong Liu^{1

2}, Tao Wang^{1

2}

Affiliations

¹ Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
² Institute of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
³ Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Abstract

RNA binding proteins (RBPs) play a key role in post-transcriptional gene regulation. They have been shown to be dysfunctional in a variety of cancers and are closely related to the occurrence and progression of cancers. However, the biological function and clinical significance of RBPs in clear cell renal carcinoma (ccRCC) are unclear. In our current study, we downloaded the transcriptome data of ccRCC patients from The Cancer Genome Atlas (TCGA) database and identified differential expression of RBPs between tumor tissue and normal kidney tissue. Then the biological function and clinical value of these RBPs were explored by using a variety of bioinformatics techniques. We identified a total of 40 differentially expressed RBPs, including 10 down-regulated RBPs and 30 up-regulated RBPs. Eight RBPs (APOBEC3G, AUH, DAZL, EIF4A1, IGF2BP3, NR0B1, RPL36A, and TRMT1) and nine RBPs (APOBEC3G, AUH, DDX47, IGF2BP3, MOV10L1, NANOS1, PIH1D3, TDRD9, and TRMT1) were identified as prognostic related to overall survival (OS) and disease-free survival (DFS), respectively, and prognostic models for OS and DFS were constructed based on these RBPs. Further analysis showed that OS and DFS were worse in high-risk group than in the low-risk group. The area under the receiver operator characteristic curve of the model for OS was 0.702 at 3 years and 0.726 at 5 years in TCGA cohort and 0.783 at 3 years and 0.795 at 5 years in E-MTAB-1980 cohort, showing good predictive performance. Both models have been shown to independently predict the prognosis of ccRCC patients. We also established a nomogram based on these prognostic RBPs for OS and performed internal validation in the TCGA cohort, showing an accurate prediction of ccRCC prognosis. Stratified analysis showed a significant correlation between the prognostic model for OS and ccRCC progression.

Keywords: RNA binding proteins; bioinformatics; clear cell renal cell carcinoma; prognostic model; survival analysis.