Elucidating the Landscape of Aberrant DNA Methylation in Hepatocellular Carcinoma

<div><p>Background</p><p>Hepatocellular carcinoma (HCC) is one of the most common cancers and frequently presents with an advanced disease at diagnosis. There is only limited knowledge of genome-scale methylation changes in HCC.</p> <p>Methods and Findings</p><p>We performed genome-wide methylation profiling in a total of 47 samples including 27 HCC and 20 adjacent normal liver tissues using the Illumina HumanMethylation450 BeadChip. We focused on differential methylation patterns in the promoter CpG islands as well as in various less studied genomic regions such as those surrounding the CpG islands, i.e. shores and shelves. Of the 485,577 loci studied, significant differential methylation (DM) was observed between HCC and adjacent normal tissues at 62,692 loci or 13% (p<1.03e-07). Of them, 61,058 loci (97%) were hypomethylated and most of these loci were located in the intergenic regions (43%) or gene bodies (33%). Our analysis also identified 10,775 differentially methylated (DM) loci (17% out of 62,692 loci) located in or surrounding the gene promoters, 4% of which reside in known Differentially Methylated Regions (DMRs) including reprogramming specific DMRs and cancer specific DMRs, while the rest (10,315) involving 4,106 genes could be potential new HCC DMR loci. Interestingly, the promoter-related DM loci occurred twice as frequently in the shores than in the actual CpG islands. We further characterized 982 DM loci in the promoter CpG islands to evaluate their potential biological function and found that the methylation changes could have effect on the signaling networks of Cellular development, Gene expression and Cell death (p = 1.0e-38), with <i>BMP4</i>, <i>CDKN2A</i>, <i>GSTP1</i>, and <i>NFATC1</i> on the top of the gene list.</p> <p>Conclusion</p><p>Substantial changes of DNA methylation at a genome-wide level were observed in HCC. Understanding epigenetic changes in HCC will help to elucidate the pathogenesis and may eventually lead to identification of molecular markers for liver cancer diagnosis, treatment and prognosis.</p> </div

Dot plots of Beta values for 5 hypo- and hypermethylated loci among the 20 top based on the Delta-beta values from the tumor (n = 27, red) versus adjacent normal comparison (n = 20, blue).

<p>Each point represents the Beta value for an individual. The median Beta value for each locus and tissue type is indicated by a line inside each box-and-whisker within the graph. Paired samples are connected by a line.</p

Top 20 hypomethylated and hypermethylated loci in HCC based on Delta-beta values.

a<p>Bonferroni Corrected p-value ^b DMR ^c Enhancer.</p

Validation results of 8 DM loci by Pyrosequencing.

<p>Gene name, Target ID (locus) by Illumina, and the correlation coefficients (r) are presented. Beta values for each individual HCC (red) and normal (blue) samples are presented by dots. The x-axis and y-axis indicate the Beta value from the Methylation450 BeadChip analysis and the methylation level by Pyrosequencing, respectively.</p

Distribution of promoter methylation levels.

<p>Genomic surroundings of the 10,775 promoter DM loci excluding the Open seas are shown <b>A.</b> in normals and <b>B.</b> in HCC tumors. The illustrative box plots present the median by a line in the box with the 25th percentile, 75th percentile and the range of the Beta values. Outlier values are shown with yellow color dots extending above or below the range markers. Density of functional loci on each genomic region is indicated on the top part of the figure. Averages of the Beta values are shown on each box plot. *previously known DMRs, cDMRs, rDMRs, Enhancers.</p

Methylation profiles of 10,775 promoter region DM loci by DMRs and in relation to CpG islands.

<p><b>A.</b> Distribution of DM loci in CpG islands and the surrounding shore (0–2 kb from promoter CpG islands), shelf (2–4 kb from promoter CpG islands) and Open sea (other regions in promoter) DM areas. <b>B.</b> DM locus distribution by known differentially methylated regions (DMRs), reprogramming specific DMRs (rDMRs), cancer specific DMRs (cDMRs) and potential novel DMRs in HCC. <b>C.</b> Unsupervised hierarchical clustering of beta values for 702 Shelves, 1,952 Shores, 982 CpG island, and 7,139 Open seas loci (rows) in 47 samples (columns). Blue and red blocks on top of the maps represent 20 adjacent normal and 27 HCC tissues, respectively, while red for the loci represents hypermethylation and blue hypomethylation.</p

Biological functions of 10 IPA networks of the genes harboring 982 differentially methylated (DM) loci in the promoter CpG islands.

<p>Italic and bold formatted genes represent hypo- and hypermethylated genes, respectively, with the bold italic formatted gene (<i>GNAS</i>) in network 7 having both hypo- and hypermethylated loci. The other genes are typically found in these networks in other studies, but not in our data set.</p

Clinical and Pathological Characteristics of 27 HCC cases.

*<p>A person had more than 2 drinks a day for 10 years or more.</p

Table2_RSK1 and RSK2 serine/threonine kinases regulate different transcription programs in cancer.XLSX

The 90 kDa ribosomal S6 kinases (RSKs) are serine threonine kinases comprising four isoforms. The isoforms can have overlapping functions in regulation of migration, invasion, proliferation, survival, and transcription in various cancer types. However, isoform specific differences in RSK1 versus RSK2 functions in gene regulation are not yet defined. Here, we delineate ribosomal S6 kinases isoform-specific transcriptional gene regulation by comparing transcription programs in RSK1 and RSK2 knockout cells using microarray analysis. Microarray analysis revealed significantly different mRNA expression patterns between RSK1 knockout and RSK2 knockout cell lines. Importantly some of these functions have not been previously recognized. Our analysis revealed RSK1 has specific roles in cell adhesion, cell cycle regulation and DNA replication and repair pathways, while RSK2 has specific roles in the immune response and interferon signaling pathways. We further validated that the identified gene sets significantly correlated with mRNA datasets from cancer patients. We examined the functional significance of the identified transcriptional programs using cell assays. In alignment with the microarray analysis, we found that RSK1 modulates the mRNA and protein expression of Fibronectin1, affecting cell adhesion and CDK2, affecting S-phase arrest in the cell cycle, and impairing DNA replication and repair. Under similar conditions, RSK2 showed increased ISG15 transcriptional expression, affecting the immune response pathway and cytokine expression. Collectively, our findings revealed the occurrence of RSK1 and RSK2 specific transcriptional regulation, defining separate functions of these closely related isoforms.</p

Table3_RSK1 and RSK2 serine/threonine kinases regulate different transcription programs in cancer.xlsx

The 90 kDa ribosomal S6 kinases (RSKs) are serine threonine kinases comprising four isoforms. The isoforms can have overlapping functions in regulation of migration, invasion, proliferation, survival, and transcription in various cancer types. However, isoform specific differences in RSK1 versus RSK2 functions in gene regulation are not yet defined. Here, we delineate ribosomal S6 kinases isoform-specific transcriptional gene regulation by comparing transcription programs in RSK1 and RSK2 knockout cells using microarray analysis. Microarray analysis revealed significantly different mRNA expression patterns between RSK1 knockout and RSK2 knockout cell lines. Importantly some of these functions have not been previously recognized. Our analysis revealed RSK1 has specific roles in cell adhesion, cell cycle regulation and DNA replication and repair pathways, while RSK2 has specific roles in the immune response and interferon signaling pathways. We further validated that the identified gene sets significantly correlated with mRNA datasets from cancer patients. We examined the functional significance of the identified transcriptional programs using cell assays. In alignment with the microarray analysis, we found that RSK1 modulates the mRNA and protein expression of Fibronectin1, affecting cell adhesion and CDK2, affecting S-phase arrest in the cell cycle, and impairing DNA replication and repair. Under similar conditions, RSK2 showed increased ISG15 transcriptional expression, affecting the immune response pathway and cytokine expression. Collectively, our findings revealed the occurrence of RSK1 and RSK2 specific transcriptional regulation, defining separate functions of these closely related isoforms.</p