Fig. 1

Identification of master epigenetic regulators of the cancer DNA methylome. a First, we conduct a pan-cancer-wide (TCGA) differential expression analysis of a comprehensive list of 212 “epigenetic enzyme” (EE) genes, defined as genes which play a role in modifying or regulating epigenetic marks, in order to identify those which exhibit consistent up- or downregulation across different cancer types. N normal, C cancer. b Since EE genes may control the epigenome, including the DNA methylome, we computed for each cancer sample two epigenetic instability indices (HyperZ and HypoZ), reflecting the global deviation in DNAm patterns from a normal reference (obtained using the corresponding normal tissue specimens). Briefly, the HyperZ index measures aberrant hypermethylation over promoter CpG islands (CGI) in a given cancer sample, whereas HypoZ measures aberrant hypomethylation over opensea probes (intergenic regions of low CpG density). c Third, we use the matched RNA-Seq and DNAm data of TCGA tumor samples to conduct a pan-cancer-wide correlation analysis between the expression levels of EE genes and these two epigenetic instability indices in order to identify EE genes whose expression variation associates with aberrant cancer DNAm. d Finally, we use causal network modeling of the EE genes which show consistent differential expression and correlation with HyperZ/HypoZ across cancer types to identify the subset of EE genes which appear to control the global variations in DNAm (HyperZ/HypoZ). The causal modeling uses partial correlations to eliminate (indirect) associations between EE gene expression and HyperZ/HypoZ which are mediated by DNAm changes driven by other EE genes