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Changes in correlation between promoter methylation and gene expression in cancer.

Moarii M, Boeva V, Vert JP, Reyal F - BMC Genomics (2015)

Bottom Line: Methylation of high-density CpG regions known as CpG Islands (CGIs) has been widely described as a mechanism associated with gene expression regulation.However, this hypermethylation is not accompanied by a decrease in expression of the corresponding genes, which are already lowly expressed in the normal genes.It may instead modify how the expression of a few specific genes, particularly transcription factors, are associated with DNA methylation variations in a tissue-dependent manner.

View Article: PubMed Central - PubMed

Affiliation: CBIO-Centre for Computational Biology, Mines Paristech, PSL-Research University, 35 Rue Saint-Honore, Fontainebleau, F-77300, France. matahi.moarii@mines-paristech.fr.

ABSTRACT

Background: Methylation of high-density CpG regions known as CpG Islands (CGIs) has been widely described as a mechanism associated with gene expression regulation. Aberrant promoter methylation is considered a hallmark of cancer involved in silencing of tumor suppressor genes and activation of oncogenes. However, recent studies have also challenged the simple model of gene expression control by promoter methylation in cancer, and the precise mechanism of and role played by changes in DNA methylation in carcinogenesis remains elusive.

Results: Using a large dataset of 672 matched cancerous and healthy methylomes, gene expression, and copy number profiles accross 3 types of tissues from The Cancer Genome Atlas (TCGA), we perform a detailed meta-analysis to clarify the interplay between promoter methylation and gene expression in normal and cancer samples. On the one hand, we recover the existence of a CpG island methylator phenotype (CIMP) with prognostic value in a subset of breast, colon and lung cancer samples, where a common subset of promoter CGIs hypomethylated in normal samples become hypermethylated. However, this hypermethylation is not accompanied by a decrease in expression of the corresponding genes, which are already lowly expressed in the normal genes. On the other hand, we identify tissue-specific sets of genes, different between normal and cancer samples, whose inter-individual variation in expression is significantly correlated with the variation in methylation of the 3' flanking regions of the promoter CGIs. These subsets of genes are not the same in the different tissues, nor between normal and cancerous samples, but transcription factors are over-represented in all subsets.

Conclusion: Our results suggest that epigenetic reprogramming in cancer does not contribute to cancer development via direct inhibition of gene expression through promoter hypermethylation. It may instead modify how the expression of a few specific genes, particularly transcription factors, are associated with DNA methylation variations in a tissue-dependent manner.

No MeSH data available.


Related in: MedlinePlus

Impact of DNA methylation in gene expression prediction. Predictive power distribution of DNA methylation for gene expression using either the average CGI methylation and least squares (orange) or the full CGI + SS profile and lasso regression (purple) shows that a more complex model allows to better predict gene expression variations in both normal and cancerous tissues
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Fig6: Impact of DNA methylation in gene expression prediction. Predictive power distribution of DNA methylation for gene expression using either the average CGI methylation and least squares (orange) or the full CGI + SS profile and lasso regression (purple) shows that a more complex model allows to better predict gene expression variations in both normal and cancerous tissues

Mentions: We observe that the full CGI + SS methylation profile is predictive of gene expression for a subset of genes in each dataset, and that this predictive power is significantly higher than using only the average CGI + SS methylation (Fig. 6, Additional files 11a, b, PBreast< 10−16, PLung=1.3×10−16, PColon=3.2×10−5). We provide in Table 5 the list of the top 50 genes based on their predictive score in cancerous breast tissues and similar lists for normal breast, lung and colon tissues in Additional file 12. Among the 2,374 genes studied, 139 genes are associated with more than one CGI + SS. For these genes, the predictive power is computed using the CGI + SS closest to the TSS. Using all the CGI + SS for these genes do not yield significant improvement over taking only the CGI + SS closest to the TSS except for breast tissues (PBreast=0.003, PLung=0.15, PColon=0.62). We also observe no association between the predictive goodness of fit R2 and the CGI + SS clusters described above (PBreast=0.48, PLung=0.47, PColon=0.44).Fig. 6


Changes in correlation between promoter methylation and gene expression in cancer.

Moarii M, Boeva V, Vert JP, Reyal F - BMC Genomics (2015)

Impact of DNA methylation in gene expression prediction. Predictive power distribution of DNA methylation for gene expression using either the average CGI methylation and least squares (orange) or the full CGI + SS profile and lasso regression (purple) shows that a more complex model allows to better predict gene expression variations in both normal and cancerous tissues
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4625954&req=5

Fig6: Impact of DNA methylation in gene expression prediction. Predictive power distribution of DNA methylation for gene expression using either the average CGI methylation and least squares (orange) or the full CGI + SS profile and lasso regression (purple) shows that a more complex model allows to better predict gene expression variations in both normal and cancerous tissues
Mentions: We observe that the full CGI + SS methylation profile is predictive of gene expression for a subset of genes in each dataset, and that this predictive power is significantly higher than using only the average CGI + SS methylation (Fig. 6, Additional files 11a, b, PBreast< 10−16, PLung=1.3×10−16, PColon=3.2×10−5). We provide in Table 5 the list of the top 50 genes based on their predictive score in cancerous breast tissues and similar lists for normal breast, lung and colon tissues in Additional file 12. Among the 2,374 genes studied, 139 genes are associated with more than one CGI + SS. For these genes, the predictive power is computed using the CGI + SS closest to the TSS. Using all the CGI + SS for these genes do not yield significant improvement over taking only the CGI + SS closest to the TSS except for breast tissues (PBreast=0.003, PLung=0.15, PColon=0.62). We also observe no association between the predictive goodness of fit R2 and the CGI + SS clusters described above (PBreast=0.48, PLung=0.47, PColon=0.44).Fig. 6

Bottom Line: Methylation of high-density CpG regions known as CpG Islands (CGIs) has been widely described as a mechanism associated with gene expression regulation.However, this hypermethylation is not accompanied by a decrease in expression of the corresponding genes, which are already lowly expressed in the normal genes.It may instead modify how the expression of a few specific genes, particularly transcription factors, are associated with DNA methylation variations in a tissue-dependent manner.

View Article: PubMed Central - PubMed

Affiliation: CBIO-Centre for Computational Biology, Mines Paristech, PSL-Research University, 35 Rue Saint-Honore, Fontainebleau, F-77300, France. matahi.moarii@mines-paristech.fr.

ABSTRACT

Background: Methylation of high-density CpG regions known as CpG Islands (CGIs) has been widely described as a mechanism associated with gene expression regulation. Aberrant promoter methylation is considered a hallmark of cancer involved in silencing of tumor suppressor genes and activation of oncogenes. However, recent studies have also challenged the simple model of gene expression control by promoter methylation in cancer, and the precise mechanism of and role played by changes in DNA methylation in carcinogenesis remains elusive.

Results: Using a large dataset of 672 matched cancerous and healthy methylomes, gene expression, and copy number profiles accross 3 types of tissues from The Cancer Genome Atlas (TCGA), we perform a detailed meta-analysis to clarify the interplay between promoter methylation and gene expression in normal and cancer samples. On the one hand, we recover the existence of a CpG island methylator phenotype (CIMP) with prognostic value in a subset of breast, colon and lung cancer samples, where a common subset of promoter CGIs hypomethylated in normal samples become hypermethylated. However, this hypermethylation is not accompanied by a decrease in expression of the corresponding genes, which are already lowly expressed in the normal genes. On the other hand, we identify tissue-specific sets of genes, different between normal and cancer samples, whose inter-individual variation in expression is significantly correlated with the variation in methylation of the 3' flanking regions of the promoter CGIs. These subsets of genes are not the same in the different tissues, nor between normal and cancerous samples, but transcription factors are over-represented in all subsets.

Conclusion: Our results suggest that epigenetic reprogramming in cancer does not contribute to cancer development via direct inhibition of gene expression through promoter hypermethylation. It may instead modify how the expression of a few specific genes, particularly transcription factors, are associated with DNA methylation variations in a tissue-dependent manner.

No MeSH data available.


Related in: MedlinePlus