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. 2021 Jun 30:2021:5568411.
doi: 10.1155/2021/5568411. eCollection 2021.

Integrated Analysis of the Roles of RNA Binding Proteins and Their Prognostic Value in Clear Cell Renal Cell Carcinoma

Bowen Wang  1 , Haoran Zhao  2 , Shaobin Ni  1 , Beichen Ding  1
Affiliations

Integrated Analysis of the Roles of RNA Binding Proteins and Their Prognostic Value in Clear Cell Renal Cell Carcinoma

Bowen Wang et al. J Healthc Eng. .

Retraction in

Abstract

Methods: We downloaded the RNA sequencing data of ccRCC from the Cancer Genome Atlas (TCGA) database and identified differently expressed RBPs in different tissues. In this study, we used bioinformatics to analyze the expression and prognostic value of RBPs; then, we performed functional analysis and constructed a protein interaction network for them. We also screened out some RBPs related to the prognosis of ccRCC. Finally, based on the identified RBPs, we constructed a prognostic model that can predict patients' risk of illness and survival time. Also, the data in the HPA database were used for verification.

Results: In our experiment, we obtained 539 ccRCC samples and 72 normal controls. In the subsequent analysis, 87 upregulated RBPs and 38 downregulated RBPs were obtained. In addition, 9 genes related to the prognosis of patients were selected, namely, RPL36A, THOC6, RNASE2, NOVA2, TLR3, PPARGC1A, DARS, LARS2, and U2AF1L4. We further constructed a prognostic model based on these genes and plotted the ROC curve. This ROC curve performed well in judgement and evaluation. A nomogram that can judge the patient's life span is also made.

Conclusion: In conclusion, we have identified differentially expressed RBPs in ccRCC and carried out a series of in-depth research studies, the results of which may provide ideas for the diagnosis of ccRCC and the research of new targeted drugs.

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Conflict of interest statement

The authors declare that there are no conflicts of interest in this research.

Figures

Figure 1
Figure 1
The differentially expressed RBPs in lung adenocarcinoma. (a) Volcano map. (b) Heat map.
Figure 2
Figure 2
Protein-protein interaction network and modules' analysis. (a) Protein-protein interaction network of differentially expressed RBPs. (b) Critical module from the PPI network. Green circles: downregulation with a fold change of more than 4; red circles: upregulation with fold change of more than 4.
Figure 3
Figure 3
Univariate COX regression analysis for the identification of hub RBPs in the training dataset.
Figure 4
Figure 4
Multivariate COX regression analysis to identify prognosis-related hub RBPs.
Figure 5
Figure 5
ccRCC prognostic data in the LOGpc database: (a) DARS, (b) LARS2, (c) NOVA2, (d) PPARGC1A, (e) RNASE2, (f) RPL36A, (g) THOC6, (h) TLR3, and (i) U2AF1L4.
Figure 6
Figure 6
Risk score analysis of the nine-gene prognostic model in the TCGA training cohort: (a) survival curve for low- and high-risk subgroups, (b) ROC curves for forecasting OS based on the risk score, (c) risk score distribution, (d) survival status, and (e) expression heat map.
Figure 7
Figure 7
Risk score analysis of the nine-gene prognostic model in the TCGA test cohort: (a) survival curve for low- and high-risk subgroups, (b) ROC curves for forecasting OS based on the risk score, (c) risk score distribution, (d) survival status, and (e) expression heat map.
Figure 8
Figure 8
Nomogram for predicting 1-, 2-, 3-, 4-, and 5-year OS of KIRC patients in the TCGA cohort.
Figure 9
Figure 9
(a) The prognostic value of different clinical parameters through single-factor COX regression analysis (P < 0.01). (b) The prognostic value of different clinical parameters through multiple regression analysis (P < 0.01).
Figure 10
Figure 10
Verification of hub RBP expression in KIRC and normal kidney tissue using the HPA database: (a) DARS, (b) TLR3, (c) LARS2, (d) NOVA2, (e) RPL36A, (f) THOC6, (g) RNASE2, and (h) U2AF1L4.
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