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How much do DNA methylation statuses affect the near genes' expression?

  • Gene Expression
  • Epigenetics
  • High-Throughput Sequencing

The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) data is available from GDC Data Portal. You can easily import the methylation and RNA-Seq data with Subio Platform. (Watch tutorials "how to import RNA-Seq data" or "DNA methylation data" for import operations.) 

This multi-omics dataset is composed of 396 Illumina HumanMethylation450K BeadChip samples (2 Solid Tissue Normal, 394 Primary Tumor)  and 406 RNA-Seq samples (32 Solid Tissue Normal, 374 Primary Tumor). And 336 patients are with both gene expression and DNA methylation data. This sounds enough to see how much the DNA methylation pattern effects on gene expression patterns.

Let's see the methylation data first. Subio Platform can draw a TSS plot of methylation sites.

TSS Plot - histogram

Methylation sites designed in the array are mostly very close to TSS (< 500bp). Scatter plot version of TSS plot gives you more information. For example, you can see the average beta values of normal samples around TSS.

TSS_BetaValues_Normal

Beta values located farther than 2000bp from TSS looks stable and binary status of methylated or unmethylated. On the other hand, the statuses near TSS look ambiguous or fragile. Let's see the average of tumor samples.

TSS_BetaValues_Tumor

The distribution of beta values looks similar. They seem to be somewhat different from Normal, but the difference is not very clear by comparing the two charts. So you can visualize how different they are on TSS plot.

TSS_DiffOfBetaValues

Now it is clear that the methylation status changing sites are limited to near TSS. But the magnitudes of changes are not big. This chart represents the average of 336 tumor samples, and the average may be converging to 0. So let's see the difference at one tumor sample.

TSS_DiffOfBetaValuesOfOne

It shows larger changes as you expected. But still, the difference is not like shifting from 0 to 1. Such ambiguous epigenetic modifications seem to be able to cause the alteration in expression level. Another information from this chart is that the fluctuations between -0.15 and 0.15 are likely to be noise, and changes whose magnitudes are larger than 0.2 looks meaningful, in this case. So I filtered out sites with <0.15 fluctuations at 333 out of 336 tumor samples. About 2/3 sites remained, and I used them for the following analysis.

Calculating Anti-Correlation between DNA methylation status and neighboring genes' expression.

Now let's see how much the methylation and gene expression patterns are anti-correlated over the 336 patients. I removed noise from the RNA-Seq data before calculating correlations. The following chart represents the distribution of correlation coefficients of gene expression patterns and the pattern of average methylation statuses of sites CpG islands within 500bp from TSS. They are biased toward negative (anti-correlation) though; most genes are in a weak relationship (around -0.3). Only some are in strong anti-correlation (around -0.7).

Met-GX 0to500 Island

This biased shape disappears if I calculate correlation coefficients between the gene expression and the average methylation in 1500-2500bp upstream and outside CpG islands patterns. So only methylation sites near TSS (<500bp) can have the strong effect on gene expression regulations.

Met-GX 1500to2500 not-Island

See the correlation coefficients patterns in the following matrix. It looks there are two types of effect of DNA methylation on gene expression regulations. The strong effect seems to require being close to TSS, but not to do being in CpG islands. On the other hand, the weak effect is specific to island sites within 1500bp upstream of TSS.

Correlation Coefficient Matrix

Genes under the strong effect of the DNA methylation status (correlation coefficient < -0.6) is only 216, which are very limited compared to those under the weak effect. But it is remarkable that 53 out of 216 are ZNF family genes. The following table is the result of GO enrichment analysis, and it says the list is super-enriched with transcription factors. The strong effect might bring the global alteration in expression levels through these specific transcription factors.

enrichedGO_genes_under_the_strong_effect

I applied hierarchical clustering with the transcription factors over stage 1 and 2 patients. The patients were divided into 2 clusters.

clustering of TFs over starge 1-2 patients

And I examined the survival curves of the two groups of early-stage patients. You see the difference in their survival rates in the second year. So the transcription factors may affect outcomes of the early stage stomach adenocarcinoma. Contrarily, they don't seem to affect the outcomes of late-stage patients.

survival curves of stage 1-2 patients

About the analysis tools

The tools I used for this case study is listed in "Product" section at the right side. Please visit for more information. 

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