Identification and Validation of Ubiquitin-Specific Proteases as a Novel Prognostic Signature for Hepatocellular Carcinoma

Abstract

Purpose: Ubiquitin-specific proteases (USPs), as a sub-family of deubiquitinating enzymes (DUBs), are responsible for the elimination of ubiquitin-triggered modification. USPs are recently correlated with various malignancies. However, the expression features and clinical significance of USPs have not been systematically investigated in hepatocellular carcinoma (HCC).

Methods: Genomic alterations and expression profiles of USPs were investigated in CbioPortal and The Cancer Genome Atlas (TCGA) Liver hepatocellular carcinoma (LIHC) dataset. Cox regression and least absolute shrinkage and selection operator (LASSO) analyses were conducted to establish a risk signature for HCC prognosis in TCGA LIHC cohort. Subsequently, Kaplan-Meier analysis, receiver operating characteristic (ROC) curves and univariate/multivariate analyses were performed to evaluate the prognostic significance of the risk signature in TCGA LIHC and international cancer genome consortium (ICGC) cohorts. Furthermore, we explored the alterations of the signature genes during hepatocarcinogenesis and HCC progression in GSE89377. In addition, the expression feature of USP39 was further explored in HCC tissues by performing western blotting and immunohistochemistry.

Results: Genomic alterations and overexpression of USPs were observed in HCC tissues. The consensus analysis indicated that the USPs-overexpressed sub-Cluster was correlated with aggressive characteristics and poor prognosis. Cox regression with LASSO algorithm identified a risk signature formed by eight USPs for HCC prognosis. High-risk group stratified by the signature score was correlated with advanced tumor stage and poor survival HCC patients in TCGA LIHC cohort. In addition, the 8-USPs based signature could also robustly predict overall survival of HCC patients in ICGC(LIRI-JP) cohort. Furthermore, gene sets enrichment analysis (GSEA) showed that the high-risk score was associated with tumor-related pathways. According to the observation in GSE89377, USP39 expression was dynamically increased with hepatocarcinogenesis and HCC progression. The overexpression of USP39 was further determined in a local HCC cohort and correlated with poor prognosis. The co-concurrence analysis suggested that USP39 might promote HCC by regulating cell-cycle- and proliferation- related genes.

Conclusion: The current study provided a USPs-based signature, highlighting its robust prognostic significance and targeted value for HCC treatment.

Keywords: hepatocellular carcinoma; molecular target; prognosis; risk signature; ubiquitin-specific proteases.

Figure 1

The genomic alterations of ubiquitin-specific proteases in HCC tissues. (A) The Oncoprint of 57 ubiquitin-specific proteases (USPs) in TCGA LIHC dataset by CbioPortal. (B) Integrated analysis of the USPs genomic alteration proportion in LIHC dataset. (C) The top mutated genes in the USPs-altered group and USPs-unaltered groups. (D, E) The Kaplan-Meier curves of the overall survival and disease-free survival for the HCC patients in USPs-altered group and USPs-unaltered groups.

Figure 2

The expression features and interactions of ubiquitin-specific proteases family in HCC. (A) The expression levels of 57 USPs in HCC tissues and normal liver tissues evaluated in TCGA datasets. (B) Spearman correlation analyses of the 57 USPs in LIHC cohort. (C) The interactions among the 57 USPs was analyzed by STRING. (D) Liver specific interaction of the 57 USPs was predicted by Networkanalyst. ***P < 0.001; **P < 0.01; *P < 0.05.

Figure 3

Consensus clusters of the USPs. (A, B) Consensus clustering model with cumulative distribution function (CDF) with k from 2 to 9; (C) The LIHC cohort stratified into two clusters (k = 2); (D) principal component analysis (PCA) of the total mRNA profiles of the two clusters; (E) Heatmap indicated the correlation of cluster 2 with clinicopathologic parameters. (F) The Kaplan-Meier curves of the overall survival of HCC patients in the two clusters. ***P < 0.001; **P < 0.01; *P < 0.05.

Figure 4

Construction of the USPs-based signature in TCGA LIHC cohort. (A) The coefficients of the 8-gene signature were calculated by multivariate Cox analysis with LASSO. (B) The distribution and median value of the risk scores in the TCGA LIHC cohort. (C) The distributions of OS status and risk score in the TCGA LIHC cohort. (D) The ROC curve was calculated to evaluate the predictive efficiency of the USPs-based signature in TCGA. (E) The Kaplan–Meier curves of overall survival for HCC patients at high-risk group and low-risk group in TCGA. (F) The correlation of the high or low risk score with clinicopathologic parameters in the TCGA LIHC cohort. ROC, receiver operator curve. ***P < 0.001; **P < 0.01.

Figure 5

Validating the prognostic value of the USPs-based signature in ICGC cohort. (A) The distribution and median value of the risk scores in the ICGC cohort. (B) The distributions of OS status and risk score in the ICGC cohort. (C) The ROC curve was calculated to evaluate the predictive efficiency of the USPs-based signature in ICGC cohort. (D) The Kaplan–Meier curves of overall survival for HCC patients at high-risk group and low-risk group in ICGC cohort. (E, F) The Kaplan–Meier curves of HCC patients at stage I&II and stage III&IV in ICGC cohort. ICGC, International Cancer Genome Consortium.

Figure 6

Identifying the USPs-based signature as an independent factor for HCC prognosis. (A) Univariate Cox analyses of the clinicopathological factors (including the risk score) and overall survival in the TCGA LIHC cohort. (B) Multivariate Cox analyses of the clinicopathological factors (including the risk score) and overall survival in the TCGA LIHC cohort. (C) Univariate Cox analyses in the ICGC cohort. (D) Multivariate Cox analyses in the ICGC cohort. ICGC, International Cancer Genome Consortium.

Figure 7

The pathways correlated with the high-risk score. The gene set enrichment analysis (GSEA) was performed in the TCGA and ICGC cohorts to explore mechanisms underlying the 8-USPs based signature. (A) Four representative KGEE pathways and hallmarks in the high–risk group of TCGA cohort; (B) Four representative KGEE pathways and hallmarks in the high–risk group of ICGC cohort. NES, Normalized enrichment score.

Figure 8

The expression features of the 8 risk genes in hepatocarcinogenesis and HCC cases at different grades in GSE89377 cohort. (A) The expression features of the eight USPs in cases at different tumor grades in GSE89377 cohort. (B) The expression features of the eight USPs in patients with dysplastic nodules and HCC in GSE89377 cohort. *P < 0.05; ***P < 0.001; ns, non-significance.

Figure 9

The protein expression features and prognostic significance of USP39 in HCC tissues. (A) The expression of USP39 in 16 pairs of HCC and adjacent tissues analyzed by western blotting. (B) Representative immunostaining images of USP39 in HCC cases with or without metastasis. (C) Representative immunostaining images of USP39 in HCC cases with high, moderate, or poor differentiation. (D) The Kaplan–Meier curves of HCC patients with high or low USP39 expression identified by immunohistochemistry. T, tumor tissues; N, adjacent tissues; ns, non-significance. ***P < 0.001; **P < 0.01; *P < 0.05.

Figure 10

The potential function of USP39 in HCC. (A, B) The significantly positively- or negatively- correlated genes with USP39 in the TCGA LIHC cohort. (C, D) The USP39-regulated functions and pathways were calculated by GSEA with GO analysis and KEGG analysis. (E) The correlation of USP39 with cell cycle and proliferation-related genes in TCGA LIHC cohort were calculated by TIMER.