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. 2021 Jan 10;13(3):3926-3944.
doi: 10.18632/aging.202360. Epub 2021 Jan 10.

Development of prognostic signature based on RNA binding proteins related genes analysis in clear cell renal cell carcinoma

Qiang Chen  1 , Zhi-Long Li  1 , Sheng-Qiang Fu  1 , Si-Yuan Wang  1 , Yu-Tang Liu  1 , Ming Ma  1 , Xiao-Rong Yang  1 , Wen-Jie Xie  1 , Bin-Bin Gong  1 , Ting Sun  1
Affiliations

Development of prognostic signature based on RNA binding proteins related genes analysis in clear cell renal cell carcinoma

Qiang Chen et al. Aging (Albany NY). .

Abstract

RNA binding proteins (RBPs) play significant roles in the development of tumors. However, a comprehensive analysis of the biological functions of RBPs in clear cell renal cell carcinoma (ccRCC) has not been performed. Our study aimed to construct an RBP-related risk model for prognosis prediction in ccRCC patients. First, RNA sequencing data of ccRCC were downloaded from The Cancer Genome Atlas (TCGA) database. Three RBP genes (EIF4A1, CARS, and RPL22L1) were validated as prognosis-related hub genes by univariate and multivariate Cox regression analyses and were integrated into a prognostic model by least absolute shrinkage and selection operator (LASSO) Cox regression analysis. According to this model, patients with high risk scores displayed significantly worse overall survival (OS) than those with low risk scores. Moreover, the multivariate Cox analysis results indicated that risk score, tumor grade, and tumor stage were significantly correlated with patient OS. A nomogram was constructed based on the three RBP genes and showed a good ability to predict outcomes in ccRCC patients. In conclusion, this study identified a three-RBP gene risk model for predicting the prognosis of patients, which is conducive to the identification of novel diagnostic and prognostic molecular markers.

Keywords: RNA binding proteins; biomarker; clear cell renal cell carcinoma; prognostic model.

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Conflict of interest statement

CONFLICTS OF INTEREST: The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
The workflow for analyzing the RBPs in ccRCC.
Figure 2
Figure 2
The differentially expressed RBPs in ccRCC. (A) Heatmap of differentially RBPs in different samples. Red represents upregulation and green represents downregulation. (B) Volcanic plot showing dysregulated RBPs in ccRCC tissue samples. ccRCC, clear cell renal cell carcinoma; RBPs, RNA-binding proteins.
Figure 3
Figure 3
Enrichment analysis of differentially expressed RBPs. (A) Top 10 enriched BP terms, CC terms, MF terms. (B) The significant KEGG signal pathways. BP, biological process; CC, cellular components; MF, molecular functions; KEGG, Kyoto Encyclopedia of Genes and Genomes.
Figure 4
Figure 4
PPI network construction and key module screening. (A) Protein-protein interaction network. (B) Significant module 1. (C) Significant module 2. Blue: down-regulation genes. Red: up-regulation genes.
Figure 5
Figure 5
Identification of prognosis related hub RBPs. (A) Significance and Hazard ratio values of differentially expressed RBPs in univariate Cox regression. (B) Identification of prognosis related hub RBPs using multivariate Cox regression analysis.
Figure 6
Figure 6
Kaplan-Meier survival analysis of hub genes. (A) EIF4A1. (B) CARS. (C) RPL22L1.
Figure 7
Figure 7
The mRNA expression profiles of hub genes. (A) The expression levels among different cancers. (B) The expression levels of the hub genes based on the published research. (C) The mRNA expression of hub genes in normal renal tissue and ccRCC.
Figure 8
Figure 8
The expression profiles of hub genes in online bioinformatics databases. (A) The protein expression of hub genes in normal renal tissue and ccRCC on the HPA database. (B) Alterations of the hub genes on the cBioportal database.
Figure 9
Figure 9
Prognostic analysis of three-gene model in the training group. The samples were divided into high- and low-risk subgroup according to the median of risk score. (A) The curve of risk score. (B) Survival status of patients. (C) Expression heatmap of three prognostic genes. (D) Survival curve for high- and low-risk subgroup. (E) ROC analysis of three-gene model. ROC, receiver operating characteristic.
Figure 10
Figure 10
Prognostic analysis of three-gene model in the testing group. The samples were divided into high- and low-risk subgroup according to the median of risk score. (A) The curve of risk score. (B) Survival status of patients. (C) Expression heatmap of three prognostic genes. (D) Survival curve for high- and low-risk subgroup. (E) ROC analysis of three-gene model.
Figure 11
Figure 11
Results of Cox regression for risk factors for ccRCC and construction of nomograms. (A) Result of univariate Cox regression. (B) Result of multivariate Cox regression. (C) The nomograms for overall survival.
Figure 12
Figure 12
The top 10 significant enriched KEGG pathways in the training group.
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