An integrative genomics approach for identifying novel functional consequences of PBRM1 truncated mutations in clear cell renal cell carcinoma (ccRCC)

Abstract

Background: Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer. Recent large-scale next-generation sequencing analyses reveal that PBRM1 is the second most frequently mutated gene harboring many truncated mutations and has a suspected tumor suppressor role in ccRCC. However, the biological consequences of PBRM1 somatic mutations (e.g., truncated mutations) that drive tumor progression in ccRCC remain unclear.

Methods: In this study, we proposed an integrative genomics approach to explore the functional consequences of PBRM1 truncated mutations in ccRCC by incorporating somatic mutations, mRNA expression, DNA methylation, and microRNA (miRNA) expression profiles from The Cancer Genome Atlas (TCGA). We performed a systematic analysis to detect the differential molecular features in a total of 11 ccRCC samples harboring PBRM1 truncated mutations from the 33 "pan-negative" ccRCC samples. We excluded the samples that had any of the five high-confidence driver genes (VHL, BAP1, SETD2, PTEN and KDM5C) reported in ccRCC to avoid their possible influence in our results.

Results: We identified 613 differentially expressed genes (128 up-regulated and 485 down-regulated genes using cutoff |log2FC| > 1 and p < 0.05) in PBRM1 mutated group versus "pan-negative" group. The gene function enrichment analysis revealed that down-regulated genes were significantly enriched in extracellular matrix organization (adjusted p = 2.05 × 10(-7)), cell adhesion (adjusted p = 2.85 × 10(-7)), and ion transport (adjusted p = 9.97 × 10(-6)). Surprisingly, 26 transcriptional factors (TFs) genes including HOXB9, PAX6 and FOXC1 were found to be significantly differentially expressed (23 over expressed TFs and three lower expressed TFs) in PBRM1 mutated group compared with "pan-negative" group. In addition, we identified 1405 differentially methylated CpG sites (targeting 1308 genes, ||log2FC| > 1, p < 0.01) and 185 significantly altered microRNAs (|log2FC| > 1, p < 0.05) associated with truncated PBRM1 mutations. Our integrative analysis suggested that methylation and miRNA alterations were likely the downstream events associated with PBRM1 truncation mutations.

Conclusions: In summary, this study provided some important insights into the understanding of tumorigenesis driven by PBRM1 truncated mutations in ccRCC. The approach may be applied to many driver genes in various cancers.

Keywords: Clear cell renal cell carcinoma (ccRCC); Driver gene; Expression; Methylation; PBRM1; microRNA.

Fig. 1

Sample filtering workflow used for integrative genomic analyses and differential expression results by comparing 11 PBRM1 mutated and 33 “pan-negative” ccRCC samples. a A sample filtering workflow was used for integrative genomic analyses. First, 548 ccRCC samples were split into PBRM1 mutated group (177 samples) and PBRM1 non-mutated group (371 samples). Five high-confidence ccRCC driver genes (VHL, BAP1, SETD2, PTEN and KDM5C) were excluded in both groups, resulting in 31 PBRM1 mutated samples and 109 “pan-negative” samples. After that, samples that have all DNA methylation, RNA-Seq, and miRNA-Seq data were extracted; resulting in 11 PBRM1 mutated samples and 33 “pan-negative” samples for further in-depth integrative analysis. b Cartoon representation of mutation types and locations in 11 PBRM1 truncation mutated samples. Five nonsense mutations (red diamond), three splice sites mutations (green round), and three frame shift deletions (purple square) were observed in 11 PBRM1 truncated mutation samples. c Volcano plot of significance of gene expression difference between PBRM1 mutated group and “pan-negative” group at gene expression levels. Each dot represents one gene. The x axis shows the gene expression difference by a log transformed fold change while the y axis shows significance by –log₁₀ transformed p-value value obtained from edgeR. A gene is called significantly and differentially expressed if its |log(FC)| > 2 and p-value < 0.05. Red dashed line shows |log(FC)| =2 or p-value = 0.05. d Bar plot of log transfer of fold change in differentially expressed transcriptional factors. 23 transcriptional factors were found to be down-regulated in PBRM1 mutated group while three transcriptional factors were found up-regulated

Fig. 2

Functional enrichment results of differentially expressed genes from RNA-Seq of PBRM1 mutation ccRCC samples. a Clustered function and pathway enrichment results of up-regulated genes in PBRM1 mutated group compared with “pan-negative” group, with p-value < 0.01 results shown. Different clusters were shown in different colors. b Clustered function and pathway enrichment results of down-regulated genes in PBRM1 mutated group compared with “pan-negative” group, with p-value < 0.001 results shown. Different clusters were shown in different colors

Fig. 3

Methylation pattern and miRNA expression pattern in PBRM1 mutated ccRCC a Volcano plot of significance of DNA methylation pattern difference (β-difference) between PBRM1 mutated group and “pan-negative” group. Each dot represents one methylation probes. The x axis shows the difference in β-value (β-difference) while the y axis shows the significance by –log transformed p-value obtained from Samr. A probe is called significantly and differentially expressed if its |β-difference| > 0.15 and p-value < 0.01. The red dashed line shows |β-difference| =0.15 or p-value = 0.01. b Heat map of differential expressed methylation probes between PBRM1 mutated group and “pan-negative” group. c Volcano plot of significance of miRNA expression differences between PBRM1 mutated group and “pan-negative” group. Each dot represents one miRNA. The x axis shows log transformed fold changes of miRNA expression while the y axis shows significance by –log₁₀ transformed p-value obtained from edgeR. A probe is called significantly and differentially expressed if its |log(FC)| > 1 and p-value < 0.05. Red dashed line |log(FC)| =1 or p-value = 0.05. d Bar plot of top ten up-regulated miRNAs and down-regulated miRNAs that revealed in PBRM1 mutated samples compared with “pan-negative” ccRCC samples

Fig. 4

Integrative analysis results of function terms and pathway enrichment. a Venn representation of the overlaps among up-regulated genes (DEG-up), down-regulated genes (DEG-down), target genes of up-regulated miRNAs (Up miRNA targets) and target genes of down-regulated miRNAs (Down miRNA targets). b Venn representation of overlaps among function and pathway enrichment results from differential methylated genes (methylation), differential expressed genes (RNA-Seq) and targets genes of differential expressed miRNA (miRNA-Seq)

Fig. 5

PBRM1 mutation specific, up-regulated and down-regulated co-expression network. Highly co-expressed genes in PBRM1 mutated groups were mapped into a protein-protein interaction network from PINA2, as reference network. 128 up-regulated genes and 33 hyper-down (hyper-methylated and down-regulated) genes were mapped into the reference network, as up-regulated co-expression network (a) and down-regulated core co-expression network. b In down-regulated core co-expression network, only first neighbors of 33 hyper-down genes in down-regulated genes were kept in network. In both networks, only genes with degree above there were kept for better version

Fig. 6

Hypothesized mechanisms of PBRM1 truncated mutation functions in the tumor genetics of ccRCC. Hyper-methylation and altered miRNAs expression were found associated with PBRM1 truncated mutation in ccRCC. Up-regulated genes and pathways were shown in red (left) while down-regulated genes and pathways were shown in blue (right)