Regulation of Canonical Oncogenic Signaling Pathways in Cancer via DNA Methylation

SIMPLE SUMMARY: Aberrant epigenetic modifications in oncogenic pathways can lead to the onset of different cancers. This study aims to explore the role of differential DNA methylation in the regulation of oncogenic signaling pathways by integrating data from multiple sources including methylome, transcriptome and clinical presentation to uncover the effect of methylation changes acting on the four most common cancers. We utilized a differential methylation-parsing pipeline, which extracted differentially methylated biomarkers based on feature selection. Extracted biomarkers were integrated with the matching RNA-Seq and clinical data to determine if these differentially methylated CpGs could serve as potential diagnostic candidates for the four most common cancers. Our results suggested differential methylation of the genes within the NRF2-PI3K pathway may lead to the presentation of various cancer and serve as potential epigenetic biomodifiers. ABSTRACT: Disruption of signaling pathways that plays a role in the normal development and cellular homeostasis may lead to the dysregulation of cellular signaling and bring about the onset of different diseases, including cancer. In addition to genetic aberrations, DNA methylation also acts as an epigenetic modifier to drive the onset and progression of cancer by mediating the reversible transcription of related genes. Although the role of DNA methylation as an alternative driver of carcinogenesis has been well-established, the global effects of DNA methylation on oncogenic signaling pathways and the presentation of cancer is only emerging. In this article, we introduced a differential methylation parsing pipeline (MethylMine) which mined for epigenetic biomarkers based on feature selection. This pipeline was used to mine for biomarkers, which presented a substantial difference in methylation between the tumor and the matching normal tissue samples. Combined with the Data Integration Analysis for Biomarker discovery (DIABLO) framework for machine learning and multi-omic analysis, we revisited the TCGA DNA methylation and RNA-Seq datasets for breast, colorectal, lung, and prostate cancer, and identified differentially methylated genes within the NRF2-KEAP1/PI3K oncogenic pathway, which regulates the expression of cytoprotective genes, that serve as potential therapeutic targets to treat different cancers.