Integrative analysis of genomic and epigenomic regulation of the transcriptome in liver cancer

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by | Oct 23, 2017 | Gastrointestinal | 0 comments

Hepatocellular carcinoma harbors numerous genomic and epigenomic aberrations of DNA copy numbers and DNA methylation. Transcriptomic deregulation by these aberrations plays key driver roles in heterogeneous progression of cancers. Here, we profile DNA copy numbers, DNA methylation, and messenger RNA expression levels from 64 cases of hepatocellular carcinoma specimens. We find that the frequencies of the aberrancies of the DNA copy-number-correlated (CNVcor) expression genes and the methylation-correlated expression (METcor) genes are co-regulated significantly. Multi-omics integration of the CNVcor and METcor genes reveal three prognostic subtypes of hepatocellular carcinoma, which can be validated by an independent data. The most aggressive subtype expressing stemness genes has frequent BAP1 mutations, implying its pivotal role in the aggressive tumor progression. In conclusion, our integrative analysis of genomic and epigenomic regulation provides new insights on the multi-layered pathobiology of hepatocellular carcinoma, which might be helpful in developing precision management for hepatocellular carcinoma patients.

Recent large-scale and multi-omics profiling of cancers has provided a systematic picture of genomic and epigenomic deregulation in these diseases. Genomic alterations due to DNA copy-number aberration or mutations occur frequently during tumorigenesis, stimulating cancer progression. Epigenetic regulation of the cancer genome by DNA methylation also plays pivotal roles in heterogeneous cancer behaviors. In particular, in hepatocellular carcinoma (HCC), genomic profiling studies have demonstrated the enormous heterogeneity of genomic and epigenomic deregulation1. In this cancer, aberrations of DNA copy number play key regulatory roles in HCC progression2,3,4, and transcriptional deregulation resulting from such aberrations is a potential driver event in HCC progression5, 6. In addition, DNA methylation profiling studies have revealed the biological and clinical significance of epigenetic regulation in HCC progression7,8,9,10,11. Several key cancer-related genes such as IGF212 and UHRF113 exert their regulatory functions by modulating DNA methylation.

However, despite the genome-wide impact of aberrations of DNA copy numbers and DNA methylation on cancers, it remains unclear whether DNA copy-number aberration is systematically related to epigenetic DNA methylations, and, if so, whether this connection plays any role in cancer progression. In this study, we profiled DNA copy numbers, DNA methylation, and messenger RNA (mRNA) expression levels in a cohort of HCC patients. To identify genes whose expression levels are regulated by genomic and/or epigenomic deregulation, we defined DNA copy-number-correlated (CNVcor) and DNA methylation-correlated (METcor) genes, based on their corresponding gene expression levels across samples, respectively. CNVcor genes represent the transcriptional deregulation dependent on DNA copy number, whereas METcor genes represent transcriptional deregulation dependent on DNA methylation. Expression of CNVcor genes was significantly correlated with expression of METcor genes, suggesting concomitant regulation of cancer transcriptomes by alterations in genomic DNA copy number in addition to epigenetic DNA methylation. Moreover, by performing multi-omics integration of CNVcor and METcor genes, we could identify distinct molecular subtypes that were significantly associated with prognostic outcomes of HCC. Further systematic analysis could identify new mutations that could be used as precision targets or biomarkers for subtype distinction.

Fig. 1
Fig. 1

Identification of DNA copy-number-correlated (CNVcor) and DNA methylation-correlated (METcor) genes in HCC. a Distribution of the correlation coefficients between the mRNA expression levels and DNA copy numbers or DNA methylation across the samples are shown, respectively. b A Venn diagram shows the counts of CNVcor and METcor genes. The counts of overlapped genes between CNVcor and METcor are indicated. c Proportional frequencies of the CNVcor or METcor genes against total gene counts in each chromosome arm are shown, respectively. d Genomic positions of DNA methylation probes are categorized based on the positional relations with CpG islands (right) and genes (left), respectively. The proportional frequencies in each category of DNA methylation for the whole genes and METcor genes are compared, respectively. e A barplot shows the frequencies of up- or down-expressed CNVcor genes or METcor genes in each sample (top). Each of CNVcor and METcor genes is categorized as upregulated (CNVcor_UP and METcor_UP) or downregulated (CNVcor_DOWN and METcor_DOWN) genes, respectively. Heatmaps show the up- (red) and down-expressed (blue) CNVcor (middle) and METcor (bottom) genes, respectively. f Dot plots show the pairwise correlations among the frequencies of CNVcor_UP, CNVcor_DOWN, METcor_UP, and METcor_DOWN genes, respectively

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Fig. 2
Fig. 2

Identification of molecular subtypes of HCC using CNVcor and METcor genes. a, b Plots show the non-negative factorization (NMF) cluster results for the CNVcor in CNV data (a) and for the METcor in MET data (b), respectively. Kaplan–Meier plot analyses for subtypes identified by NMF clustering of the CNVcor and METcor genes are shown for overall survival (OS) and time to tumor recurrence (TTR), respectively. c Heatmaps show the expression patterns of subtypes identified by iCluster analysis. The subtypes identified by the CNVcor or METcor genes using NMF cluster methods are indicated with colored bars (top). The frequencies of aberrant expression of CNVcor_UP, CNVcor_DOWN, METcor_UP, and METcor_DOWN genes in each subtype are shown (bottom). d Kaplan–Meier plot analyses for subtypes identified by iCluster (iCl1, iCl2, and iCl3) are shown for overall survival (OS) and time to tumor recurrence (TTR), respectively

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Fig. 3
Fig. 3

Validation of molecular subtypes using an independent TCGA data. a Overall distributions of correlation coefficients of between the mRNA expression levels and DNA copy numbers or DNA methylation are shown, respectively. b A Venn diagram shows the counts of the DNA coy number-correlated genes (CNVcor) and the DNA methylation-correlated genes (METcor) genes. The counts of the overlapped genes between CNVcor and METcor are indicated. c Proportion of the CNVcor or METcor genes against total count of genes in each chromosome arm are shown. d Composition of DNA methylation probes in the whole genes and METcor genes are compared. Genomic positions of DNA methylation probes are categorized based on the relations with CpG island regions (right) and gene regions (left), respectively. e A plot shows the correlation between the frequencies of CNVcor and METcor genes in each sample. f A heatmap shows the expression patterns of the differentially expressed genes among the subtypes identified by iClusterPlus analysis (top). The frequencies of CNVcor_UP, CNVcor_DOWN, METcor_UP, and METcor_DOWN genes are shown (bottom). g Violin plots indicate the frequencies of CNVcor_UP, CNVcor_DOWN, METcor_UP, and METcor_DOWN genes are shown in each subtype of C1, C2, and C3, respectively. h Kaplan–Meier plot analyses of the HCC subtypes for overall survival (OS) and recurrence-free survival (RFS) are shown, respectively

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Fig. 4
Fig. 4

Coordinated aberrations of DNA copy number and DNA methylation in liver cancer. a, b The SNU and LIHC data sets of DNA copy numbers or DNA methylation are integrated by applying “combat” method as described in “Methods.” Aberrations of DNA copy numbers or DNA methylation were determined with cutoff fold difference >0.2 compared to those of the average values of the non-tumoral tissues, respectively. Directional alterations of DNA copy-number gain (CNVgain) and loss (CNVloss) and DNA hypermethylation (METhyper) and hypomethylation (METhypo) in each sample are plotted, respectively. c Overall frequencies of CNV aberration including CNVgain and CNVloss and DNA methylation including METhyper and METhypo are plotted. dg Plots show...

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