Identification of prognostic chromatin-remodeling genes in clear cell renal cell carcinoma

Results

Identification of subgroups with distinct prognoses in TCGA-KIRC patients based on consensus clustering

After data processing, a total of 496 TCGA-KIRC patients were included, and 86 chromatin-remodeling genes were accepted as the definitive input in a first-step consensus clustering analysis. According to the comparison of the cumulative distribution function (CDF) curve from 2 to 10 category numbers (Figure 1B, 1C), two major clusters (cluster A and cluster B) were identified for which model stabilization became the highest with the optimal classification accuracy of 2 categories in the consensus matrix (Figure 1A). Patients belonging to cluster B (n=290) exhibited significantly better prognostic values for overall survival (OS) (P= 0.00026) and progression-free survival (PFS) (P= 0.00019), while patients in cluster A (n=206) displayed a strong tendency for poor clinical outcomes (Figure 1D, 1E). Univariate and multivariate Cox regression analyses indicated that in addition to age, clinical stage and differentiation grade, cluster B was a significant factor correlated with OS and PFS endpoints in TCGA-KIRC patients (hazard ratio (HR): 0.667; 95% confidence interval (CI): 0.535-0.831; P <0.001; Table 1). As the two clusters show substantial differences in survival outcomes, their associations with clinical parameters and vital mutations were also estimated. Clearly, cluster B was significantly related to a relatively low clinical stage and good histological differentiation (Supplementary Table 1). However, the statuses of some top mutant genes in renal carcinoma patients, including VHL, PBRM1, TP53 and MTOR, which were collected from the Catalogue of Somatic Mutations in Cancer (COSMIC) database, did not show evident differences between the two clusters.

Figure 1. Consensus clustering algorithm identified two clusters with prognostic value. (A) 496 TCGA-KIRC patients were group into a consensus matrix with Cluster A (n=206) and Cluster B (n=290) based on 86 chromatin-remodeling factors and consensus clustering algorithm. (B) Consensus matrix among cumulative distribution function (CDF) curves from 2 to 10 clusters. (C) Relative changes in delta area under the CDF curve for each k category from 2 to 10. (D) Comparison of overall prognostic differences between Cluster A and Cluster B upon all stage patients. (E) Comparison of progression-free prognostic differences between Cluster A and Cluster B upon all stage patients.

Table 1. Univariate and multivariate cox regression of Cluster A and Cluster B for overall survival and progression-free survival in TCGA-KIRC (n=496).

Variable		Univariate analysis		Multivariate analysis
		HR (95% CI)	P value	HR (95% CI)	P value
TCGA-KIRC OS (N=496)
Cluster	Cluster B vs Cluster A	0.667(0.535-0.831)	< 0.001	0.767(0.613-0.96)	0.02
Age	Continuous	1.03(1.017-1.044)	< 0.001	1.032(1.018-1.047)	< 0.001
Gender	Male vs Female	0.961(0.766-1.206)	0.731
Stage	Stage IV vs Stage I-III	3.037(2.419-3.812)	< 0.001	2.672(2.102-3.398)	< 0.001
Grade	Grade III-IV vs Grade I-II	2.06(1.595-2.661)	< 0.001	1.565(1.196-2.048)	0.001
TCGA-KIRC PFS (n=496)
Cluster	Cluster B vs Cluster A	0.695(0.573-0.843)	< 0.001	0.799(0.656-0.973)	0.025
Age	Continuous	1.021(1.009-1.032)	< 0.001	1.021(1.009-1.033)	< 0.001
Gender	Male vs Female	1.118(0.912-1.371)	0.282
Stage	Stage IV vs Stage I-III	3.788(3.073-4.668)	< 0.001	3.431(2.761-4.263)	< 0.001
Grade	Grade III-IV vs Grade I-II	2.011(1.618-2.499)	< 0.001	1.652(1.32-2.069)	< 0.001

Characterized chromatin-remodeling factor screening by the random forest algorithm

After multiple cross-validations of the random forest (RF) process, the relationship between the number of selected variables and model accuracy based on the consensus cluster A and cluster B grouping was evaluated (Figure 2A). The cut-off value of the model’s accuracy was defined as 0.9, and the accuracy reached 90% when at least 3 variables were included in the model (Supplementary Table 2). The model achieved its highest accuracy when 38 variables were included by rank, and the top three variables were CNOT1, SIN3A and BPTF (Supplementary Table 3), which means that these three factors could represent the original chromatin-remodeling model with 90% accuracy. According to the RF algorithm, the importance of the contributions of all 86 chromatin-remodeling factors in the model was also calculated (Figure 2B). The top three variables of the green features, which are features designated as having robust importance, were CNOT1, SIN3A and BPTF. Agreement between the two approaches validated the consistency and enhanced the confidence of the result. The Z-score-processed expression data of the 86 chromatin-remodeling factor dendrogram clustering heatmap based on prespecified groups were utilized to discover the differences in CNOT1, SIN3A and BPTF expression levels between cluster A and cluster B (Figure 2C). Consensus clustering based on BPTF, SIN3A and CNOT1 was also performed to obtain cluster C and cluster D. Interestingly, we found that BPTF, SIN3A and CNOT1 were all highly expressed in cluster B, a group previously associated with relatively good outcomes, and the patient proportions and distributions of cluster C and cluster D were consistent with those of cluster A and cluster B. Then, comparisons of OS and PFS between cluster C and cluster D for the 496 TCGA-KIRC patients were performed to verify conformity. As shown in Figure 2D, 2E, compared with those in cluster C, the ccRCC patients in cluster D had better OS (P= 0.0011) and PFS (P= 0.00031). The risk-predicted accuracy of the combined model including clinical stage, BPTF, SIN3A and CNOT1 was higher than that of the univariate clinical stage model for one-year, three-year and five-year OS (Supplementary Figure 1A, 1B). The prediction of long-term survival varied even more between the two models. In summary, the discovery that a model based on BPTF, SIN3A and CNOT1 could faithfully represent the model resulting from the original input of chromatin-remodeling factors was confirmed in a variety of ways.

Figure 2. Characterized chromatin-remodeling factors selection based on random forest algorithm. (A) The relationship between the number of variables and model accuracy after multiple cross-validations of the random forest process was shown. The auxiliary line was added at 0.9 accuracy and the first point indicated 2 variables while the following points added one variable compared to the previous one. (B) The importance of each variable in model contribution was shown. The horizontal axis represents the position of 86 chromatin-remodeling factors in the rank of high to low importance from right to left. Red features were confirmed unimportant, yellow features were designated as tentative and green features were confirmed important. Three blue boxplots mapped to minimal, mean and maximum Z scores of shadow attributes respectively. (C) Z score gene expression level of 86 chromatin-remodeling factors among consensus cluster A and cluster B. Clinicopathological stage, outcome status and groups (cluster C and cluster D) of consensus cluster based on BPTF, CNOT1, and SIN3A were annotated in the heatmap. (D) Comparison of overall prognostic differences between Cluster C and Cluster D upon all stage patients. (E) Comparison of progression-free prognostic differences between Cluster C and Cluster D upon all stage patients.

Survival analysis and correlation analyses of clinical parameters with BPTF, SIN3A and CNOT1

The OS and PFS estimations of BPTF, SIN3A and CNOT1 were performed for 496 TCGA-KIRC patients. After dividing ccRCC patients into three equal quantile divisions based on selected gene expression, we found that compared with the low expression group, the high BPTF expression group exhibited better OS (P< 0.0001) and PFS (P< 0.0001) (Figure 3A, 3B). Likewise, ccRCC patients with high CNOT1 expression had better OS (P< 0.0001) and PFS (P= 0.00018) than those with low expression (Figure 3C, 3D), and patients in the high SIN3A expression group also had better OS (P= 0.00081) and PFS (P= 0.00015) outcomes than those in the low expression group (Figure 3E, 3F). Associations between the expression levels of these three characterized chromatin-remodeling genes and clinicopathological parameters were also evaluated. As the clinical stage advanced, the expression levels of BPTF, SIN3A and CNOT1 appeared to decrease gradually (Figure 4A–4C). In regard to differentiation grade, significantly reduced expression was found in poorly differentiated tumor tissue compared with normal renal tissue or neoplastic tumor tissue with a level I grade (Figure 4D–4F).

Figure 3. Prognostic analysis of three characterized chromatin-remodeling factors upon 496 TCGA-KIRC patients. Comparison of overall and progression-free prognostic differences between two groups divided by BPTF (A, B), CNOT1 (C, D) and SIN3A (E, F) expression levels based on median expression level.

Figure 4. Association between three characterized chromatin-remodeling factors expression levels and clinicopathological parameters. Differentiated expression of BPTF, CNOT1 and SIN3A between normal renal tissue, disparate clinicopathological stages (A–C) and clinicopathological grade (D–F). The Wilcoxon test was used for group comparison.

Functional annotation of CNOT1, SIN3A and BPTF and property analysis of their highly correlated genes

Since the results of the survival analysis and correlation analysis between clinicopathological parameters and BPTF, SIN3A and CNOT1 in TCGA-KIRC were highly consistent, we investigated the potential mechanisms of these three chromatin-remodeling genes and their highly correlated genes (Pearson r>0.5; P<0.001) in ccRCC. As shown in Figure 5A–5C, gene ontology (GO) analysis indicated that BPTF, SIN3A and CNOT1 function in the biological process of chromatin regulation, which is consistent with current knowledge. In addition, some of their correlated genes were found to be involved in the process of utilizing the autophagic mechanism. Furthermore, the top 5 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis terms indicated that genes associated with ubiquitin-mediated proteolysis pathways were relatively enriched (Figure 5D–5F). Because the genetic annotations and gene sets both largely associated with protein degradation pathways, we focused on the properties of their intersected genes. The Venn diagram shown in Figure 6A illustrates the number and overlap of the significantly highly correlated genes of BPTF, SIN3A and CNOT1 (Pearson r>0.6; P<0.001) in ccRCC patients. Univariate Cox regression analysis of OS was utilized to evaluate the prognostic value of 165 intersected genes. We found that the majority of these genes (92.7%; 153/165) significantly predicted a good prognosis, while only a small group of them (1.8%; 3/165) were significantly associated with poor survival outcomes or were nonsignificant (5.5%; 9/165) in ccRCC (Figure 6B). Comprehensive GO analysis performed on the 165 intersected genes suggested that processes related to posttranscriptional gene silencing regulation and protein polyubiquitination were the top enriched processes (Figure 6C). An interactive network of the top 15 enriched pathways among the intersected genes determined by KEGG analysis was constructed, and it was very consistently found that ubiquitin-mediated proteolysis pathways ranked as the second highest score in the enrichment analysis (Supplementary Figure 2A). Hence, we hypothesized that BPTF, SIN3A and CNOT1 all play roles in the occurrence and development of ccRCC by affecting autophagy regulation and activating ubiquitin-mediated proteolysis, which might account for their good prognostic value.

Figure 5. Function prediction of three characterized chromatin-remodeling factors. (A–C) Top 10 biological process enrichment results of genes in which correlation with BPTF, CNOT1 or SIN3A were greater than 0.5. (D–F) Top 5 KEGG pathway enrichment results of genes which correlation with BPTF, CNOT1 and SIN3A were greater than 0.5. The results were ranked by the enrichment scores and displayed corresponding to -log10(q-value) and gene ratio enriched in the selected pathways.

Figure 6. The properties of the intersected genes. (A) Venn diagram shows the number of intersection genes of genes in which correlation with BPTF, CNOT1, and SIN3A was greater than 0.6. (B) Univariate Cox regression results for the overall survival of all 165 intersected genes were displayed by ratio pie plot. "*" means the statistic P-value was significant (P<0.05). (C) Top 20 results of gene ontology analysis upon all 165 intersected genes.

Prediction of YY1 as a transcriptional factor of the intersected genes

The iRegulon plugin was used to predict the putative transcription factors of all 165 intersected genes with Cytoscape Java software. According to iRegulon, YY1 was the most likely transcription factor of these genes, and the predictive results of other regulators were not sufficient with strong evidence. After excluding genes that did not match YY1 (44/165; blue module) and genes with a correlation factor less than 0.3 with YY1 by Pearson’s correlation analysis (11/165; gray module), the remaining genes showed at least two evidence-based relationships with YY1, including enriched motif mapping information obtained from the Encyclopedia of DNA Elements (ENCODE), JASPAR and HOMER databases, ChIP-seq track signal calculations and correlation analysis (Figure 7). Four out of the top 10 enriched pathways identified by KEGG analysis were verified by single-gene YY1 gene set enrichment analysis (GSEA), the TGF-beta signaling pathway, the Wnt signaling pathway, RCC and ubiquitin-mediated proteolysis (Supplementary Figure 2B); these results were consistent with the enrichment results for the intersected genes (Supplementary Figure 2A), which could be considered additional evidence supporting YY1 as the potential transcriptional factor due to the similar pathway annotations. Five E3 ubiquitin ligase genes, CBL, UBE4B, TRAF6, HUWE1, and PIAS1, that were enriched in the ubiquitin-mediated proteolysis pathway identified by the previous interactive KEGG analysis (Supplementary Figure 2A), were predicted to be regulated by YY1. Because ubiquitin E3 ligases have an impact on almost every aspect of eukaryotic biological processes by promoting protein ubiquitination and degradation, it was necessary to predict the possible substrates of CBL, UBE4B, TRAF6, HUWE1 and PIAS1 to elucidate their mechanisms. Based on five relationship predictive methods provided by the UbiBrowser online tool, some potential targets were found and are listed in Supplementary Figure 3, and many of them are best known as oncoproteins in multiple cancer types, such as proteins in the JAK family, AKT1 and BRAF. As shown in Supplementary Figure 4, OS and PFS analyses with patients stratified by the median cut-off values for the five E3 ubiquitin ligases were also performed for the 496 TCGA-KIRC patients. Obviously, patients with high CBL, UBE4B, TRAF6, HUWE1 or PIAS1 expression exhibited better ccRCC outcomes than those with corresponding low expression.

Figure 7. Prediction of YY1 as intersected genes regulator. All 165 intersected genes predictive network of transcription factor YY1 performed by iRegulon. Genes in the left circle were found regulated by YY1 according to both motif's matching and ChIP-seq tracks signals. Genes in the right circle were found regulated by YY1 based on ChIP-seq tracks signals. Genes in the inner right circle were found regulated by YY1 based on motif's matching. Genes in blue modules were not mapped to YY1. Genes in grey color are those that are less than 0.3 correlated with YY1.

Relationship predictions for YY1 and the characterized chromatin remodelers

Another function encoded in iRegulon was exploited to query the targets of BPTF, a transcription-associated chromatin remodeler. According to MSigDB, GeneSigDB and Ganesh Clusters, YY1 and CDKN1A were predicted to be in the top 20 matching results (Figure 8A). Studies have identified CNKN1A as a target gene of YY1 that binds to the BRCA2 protein to have a role in tumor suppression [25]. Through experimental determination and text mining performed with the STRING database, confirmed co-expression relationships were found among the expression of BPTF, SIN3A and YY1 with SIN3A acting as the crosslinking bridge, but CNOT1 did not show any connectivity in the protein network (Figure 8B). One-year, three-year and five-year receiver operating characteristic (ROC) curves of a combined model including clinical stage, BPTF, SIN3A and CNOT1 and YY1 were constructed, and by comparison, this model had better risk-prediction performance than the previous two models, especially for long-term OS (Supplementary Figure 1A, 1B). The results of Pearson’s correlation analyses of BPTF, SIN3A, CNOT1 and YY1 in TCGA-KIRC patients were all positive and significant, and only the coefficient between CNOT1 and YY1 was relatively low (Figure 8C), which was consistent with the protein interaction results. Significantly positive correlations among BPTF, SIN3A, CNOT1 and YY1 could also be acquired with a TCGA pan-cancer analysis performed by Gene Expression Profiling Interactive Analysis (GEPIA) (Supplementary Figure 5); however, their prognostic values, which were consistent in ccRCC, were not as consistent across other cancer types (Supplementary Table 4). Finally, high expression of YY1 was associated with relatively good OS in TCGA-KIRC patients (Figure 8E), which implied that there might be a distinct interaction relationship among BPTF, SIN3A, CNOT1 and YY1 in ccRCC patients that leads to an unclear tumor-suppressive function that promotes improved clinical outcomes.

Figure 8. Relationship between characterized chromatin-remodelers and YY1 (A) Top 20 predicted target genes of BPTF. (B) The interactive relationship between BPTF, SIN3A and YY1 produced by STRING. (C) Pearson correlation between the expression levels of BPTF, CNOT1, SIN3A and YY1 upon 496 TCGA-KIRC patients. (D) Comparison of overall prognostic differences between YY1 high and low expression groups by median cut-off value.

Validation in ICGC dataset

We downloaded another RNA-Seq dataset from ICGC which containing 91 patients with renal cancer for validation. Patients in ICGC dataset can be divided into 3 groups with differential expression of BPTF, CNOT1 and SIN3A by consensus clustering. Patients with low BPTF, CNOTA and SIN3A expression have significantly worse OS and no pathways were enriched from KEGG database. Patients with relatively median or high expression of BPTF, CNOT1 and SIN3A suffer better OS and ubiquitin-mediated proteolysis pathway was enriched (NES=1.78, P=0.000) as one of the top 10 enrichment, which was consistent with the result acquired from TCGA (Supplementary Figure 6). Furthermore, patients with high expression level of BPTF and SIN3A have significantly better OS but the result of CNOT1 was insignificant. The expression levels of BPTF, CNOT1 and SIN3A significantly decreased in stage IV patients compared with stage I patients. Since BPTF, CNOT1 and SIN3A were identified as protective factors in the progression of ccRCC in TCGA dataset, that conclusion was validated in ICGC dataset (Supplementary Figure 7). Using iRegulon plugin of cytoscape, YY1 was identified as the most likely predicted transcriptional factor of overlapped correlated genes according to both motif's matching and ChIP-seq tracks signals. Pearson correlation between the expression of BPTF, CNOT1, SIN3A, YY1 and the 5 E3-ligases were significantly high, which suggested closed relationships of them. High expression of CBL and HUWE1 indicated significantly better OS while the similar prognostic tendency could be obtained in PIAS1, TRAF6 and UBE4B but were not significant. The results of transcriptional factor's prediction, correlation analysis and survival analysis in ICGC dataset were consistent with TCGA (Supplementary Figure 8).

Abbreviations

ccRCC: clear cell renal cell carcinoma; TCGA-KIRC: The Cancer Genome Atlas-Kidney Renal Cell Carcinoma; HumanTFDB: Human Transcription Factor Database; FPKM: fragment per kilobase per million; RPKM: reads per kilobase per million; MAF: mutational annotation format; MAD: median absolute deviation; AUC: areas under the curve; GEPIA: Gene Expression Profiling Interactive Analysis; FDR: false discovery rate; CDF: cumulative distribution function; GSEA: gene-set enrichment analysis; KEGG: Kyoto Encyclopedia of Genes and Genomes; OS: overall survival; PFS: progression-free survival; COSMIC: Catalogue of Somatic Mutations in Cancer; GO: Gene Ontology; NURF: nucleosome-remodeling factor; ICGC: International Cancer Genome Consortium.

Author Contributions

ZL and TL conceived the study. CW and YY collected data. YY, NW, TH, ZS JX and JY participated in statistical analysis. CW and YY wrote the manuscript. All authors reviewed the manuscript.

Acknowledgments

I would like to extend my sincere thanks to my supervisor, Dr. Tao Liu, a responsible and resourceful scholar, who has helped me finish the manuscript with valuable guidance.

Conflicts of Interest

We declare no conflicts of interest for this manuscript.

Funding

The study was received funding from National Natural Science Foundation of China (81101938).

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