Prognostic Role of the Ubiquitin Proteasome System in Clear Cell Renal Cell Carcinoma: A Bioinformatic Perspective

Abstract

Background: Clear cell renal cell carcinoma (ccRCC) is a common malignant tumor of the urinary system. The ubiquitin proteasome system (UPS) plays an important role in the generation, metabolism and survival of tumor. We are aimed to make a comprehensive exploration of the UPS's role in ccRCC with bioinformatic tools, which may contribute to the understanding of UPS in ccRCC, and give insight for further research. Methods: The UPS-related genes (UPSs) were collected by an integrative approach. The expression and clinical data were downloaded from TCGA database. R soft was used to perform the differentially expressed UPSs analysis, functional enrichment analysis. We also estimated prognostic value of each UPS with the help of GEPIA database. Two predicting models were constructed with the differentially expressed UPSs and prognosis-related genes, respectively. The correlations of risk score with clinical characteristics were also evaluated. Data of GSE29609 cohort were obtained from GEO database to validate the prognostic models. Results: We finally identified 91 differentially expressed UPSs, 48 prognosis related genes among them, and constructed a prognostic model with 18 UPSs successfully, the AUC was 0.760. With the help of GEPIA, we found 391 prognosis-related UPSs, accounting for 57.84% of all UPSs. Another prognostic model was constructed with 28 prognosis-related genes of them, and with a better AUC of 0.825. Additionally, our models can also stratify patients into high and low risk groups accurately in GSE29609 cohort. Similar prognostic values of our models were observed in the validated GSE29609 cohort. Conclusions: UPS is dysregulated in ccRCC. UPS related genes have significant prognostic value in ccRCC. Models constructed with UPSs are effective and applicable. An abnormal ubiquitin proteasome system should play an important role in ccRCC and be worthy of further study.

Keywords: bioinformatics; clear cell renal cell carcinoma (ccRCC); prognosis; the ubiquitin proteasome system (UPS).

Figure 1

Overview flowchart of this study. Exploration of the prognostic role of UPS in ccRCC from a bioinformatic perspective.

Figure 2

The expression profiles of UPSs between tumor samples and normal samples in TCGA cohort of ccRCC. (A) Volcano plot of 676 UPSs. The vertical axis indicates the -log10 False Discovery Rate (FDR), and the horizontal axis indicates the log2 fold change (FC). The red dots and the green dots represent up- and down-regulated genes, respectively (P-value < 0.05 and |log2(FC)| > 1). (B) Heatmap of 91 differentially expressed UPSs. Red and green indicate higher expression and lower expression, respectively. (C) Box plot of the expression of 91 differentially expressed UPSs between tumor and normal tissues, tumor in red and normal in green.

Figure 3

Functional enrichment of the differentially expressed UPSs. (A) The top 30 significant terms of GO function enrichment. BP biological process, CC cellular component, MF molecular function. (B) The GO circle shows the scatter map of the log FC of the specified gene. (C) The terms of KEGG analysis with statistical significance. (D) The KEGG circle shows the scatter map of the log FC of the specified gene. The higher the z-score value indicated, the higher expression of the enriched pathway.

Figure 4

Model 1: prognostic signature constructed with 18 differentially expressed UPSs. (A) Kaplan-Meier curves of OS in the high- and low-risk groups stratified by the median risk score. (B) Heatmap of the expression profile of the model genes in two groups. (C) Distribution of the risk scores of ccRCC patients. (D) Survival status of patients in different groups, red dots denote patients that are dead, and green dots denote patients that are alive. (E) A forest plot of univariate Cox regression analysis in the cohorts. (F) A forest plot of multivariate Cox regression analysis in the cohorts.

Figure 5

Gene set enrichment analysis in different groups identified by Model 1. The top 5 pathways enriched in the high-risk group and low-risk group, respectively.

Figure 6

Prognostic value of all UPS-related genes exploring with GEPIA. (A) Dot plot to show the large proportion of UPSs with significant prognostic value, significant genes are shown in orange. (B) Dot plot to show the UPSs with outstanding prognostic value, red dots represent genes when log2 hazard rate (HR) > 1, green dots represent genes when log2 hazard rate (HR) < -1. (C-F) Kaplan-Meier curves of OS of representative genes with outstanding prognostic value.

Figure 7

Correlation of the expression of UPSs with tumor stages. (A-D) 4 representative genes that have negative correlations with tumor stages. They are TRIM2 (A), OTUD7A (B), RCHY1 (C), DCAF11 (D). (E-H) 4 representative genes that have positive correlations with tumor stages. They are CDCA3 (E), UBE2C (F), CDC20 (G), FBXL6 (H).

Figure 8

Model 2: prognostic signature constructed with 28 prognosis related UPSs. (A) Kaplan-Meier curves of OS in the high- and low-risk groups stratified by the median risk score. (B) Heatmap of the expression profile of the model genes in two groups. (C) Distribution of the risk scores of ccRCC patients. (D) Survival status of patients in different groups, red dots denote patients that are dead, and green dots denote patients that are alive. (E) A forest plot of univariate Cox regression analysis in the cohorts. (F) A forest plot of multivariate Cox regression analysis in the cohorts.

Figure 9

Correlations of risk score with clinical parameters with statistical significance. Correlations of risk score with grade in Model 1 (A) and Model 2 (B). Correlations of risk score with stage in Model 1 (C) and Model 2 (D). Correlations of risk score with T stage in Model 1 (E) and Model 2 (F). Correlations of risk score with N stage in Model 1 (G) and Model 2 (H). Correlations of risk score with M stage in Model 1 (I) and Model 2 (J).

Figure 10

ROC curves and external validations of Model 1 and Model 2. ROC curves of Model 1 (A) and Model 2 (C). Kaplan-Meier curves of OS in the high- and low-risk groups stratified by the median risk score of Model 1 (B) and Model 2 (D) in GSE29609 cohort.