Aim: We present here a novel method that enables unraveling the interplay between gene expression and DNA methylation in complex diseases such as cancer.
Materials & methods: The method is based on self-organizing maps and allows for analysis of data landscapes from 'governed by methylation' to 'governed by expression'.
Results: We identified regulatory modules of coexpressed and comethylated genes in high-grade gliomas: two modes are governed by genes hypermethylated and underexpressed in IDH-mutated cases, while two other modes reflect immune and stromal signatures in the classical and mesenchymal subtypes. A fifth mode with proneural characteristics comprises genes of repressed and poised chromatin states active in healthy brain. Two additional modes enrich genes either in active or repressed chromatin states.
Conclusion: The method disentangles the interplay between gene expression and methylation. It has the potential to integrate also mutation and copy number data and to apply to large sample cohorts.
Keywords: cancer heterogeneity; gene regulation; integrative bioinformatics; machine learning; molecular subtypes; transcriptome and methylome.