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. 2021 Jan 1;18(4):953-963.
doi: 10.7150/ijms.50704. eCollection 2021.

Integral Analysis of the RNA Binding Protein-associated Prognostic Model for Renal Cell Carcinoma

Xin Qin  1 , Zhengfang Liu  1 , Keqiang Yan  1 , Zhiqing Fang  1   2 , Yidong Fan  1
Affiliations

Integral Analysis of the RNA Binding Protein-associated Prognostic Model for Renal Cell Carcinoma

Xin Qin et al. Int J Med Sci. .

Abstract

RNA binding protein (RBPs) dysregulation has been reported in various malignant tumors and plays a pivotal role in tumor carcinogenesis and progression. However, the underlying mechanisms in renal cell carcinoma (RCC) are still unknown. In the present study, we performed a bioinformatics analysis using data from TCGA database to explore the expression and prognostic value of RBPs. We identified 125 differently expressed RBPs between tumor and normal tissue in RCC patients, including 87 upregulated and 38 downregulated RBPs. Eight RBPs (RPL22L1, RNASE2, RNASE3, EZH2, DDX25, DQX1, EXOSC5, DDX47) were selected as prognosis-related RBPs and used to construct a risk score model. In the risk score model, the high-risk subgroup had a poorer overall survival (OS) than the low-risk subgroup, and we divided the 539 RCC patients into two groups and conducted a time-dependent receiver operating characteristic (ROC) analysis to further test the prognostic ability of the eight hub RBPs. The area under the curve (AUC) of the ROC curve was 0.728 in train-group and 0.688 in test-group, indicating a good prognostic model. More importantly, we established a nomogram based on the selected eight RBPs. The eight selected RBPS have predictive value for RCC patients, with potential applications in clinical decision-making and individualized treatment.

Keywords: Overall survival; Prognostic model; RNA binding proteins; Renal cell carcinoma.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interest exists.

Figures

Figure 1
Figure 1
Whole procedures for analyzing RBPs in RCC.
Figure 2
Figure 2
The differently expressed RBPs in RCC. (A) Heat map; (B) Volcano plot.
Figure 3
Figure 3
GO and KEGG pathway enrichment analyses of aberrantly expressed RBPs in RCC. (A) GO enrichment analysis of upregulated RBPs; (B) GO enrichment analysis of downregulated RBPs; (C) KEGG pathway enrichment analysis of upregulated RBPs; (D) KEGG pathway enrichment analysis of downregulated RBPs.
Figure 4
Figure 4
Protein-protein interaction network and modules analysis. (A) PPI network of differently expressed RBPs; (B) Critical module from PPI network. Green circles: downregulation; red circles: upregulation.
Figure 5
Figure 5
Univariate Cox regression analysis to identify hub RBPs.
Figure 6
Figure 6
Multivariate Cox regression analysis for identification of prognosis-related hub RBPs.
Figure 7
Figure 7
Risk score analysis of risk model in train-group and test-group. (A) Survival curve for low- and high-risk subgroups in train-group; (B) ROC curves for forecasting OS in train-group; (C) Risk score, expression heat map and survival status in train-group; (D) Survival curve for low- and high-risk subgroups in test-group; (E) ROC curves for forecasting OS in test-group; (F) Risk score, expression heat map and survival status in test-group.
Figure 8
Figure 8
(A) Univariate Cox regression analysis for different clinical parameters; (B) Multivariate Cox regression analysis for different clinical parameters.
Figure 9
Figure 9
Nomogram for predicting OS of RCC patients.
Figure 10
Figure 10
Validation of the prognostic value of hub RBPs in RCC patients by Kaplan Meier-plotter.
Figure 11
Figure 11
GSEA analysis of eight hub RBPs with C2 KEGG gene sets.
See this image and copyright information in PMC

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