Abstract
Genome-wide DNA methylation and gene expression are commonly altered in pediatric acute lymphoblastic leukemia (PALL). Integrated network analysis of cytosine methylation and expression datasets has the potential to provide deeper insights into the complex disease states and their causes than individual disconnected analyses. With the purpose of identifying reliable cancer-associated methylation signal in gene regions from leukemia patients, we present an integrative network analysis of differentially methylated (DMGs) and differentially expressed genes (DEGs). The application of a novel signal detection-machine learning approach to methylation analysis of whole genome bisulfite sequencing (WGBS) data permitted a high level of methylation signal resolution in cancer-associated genes and pathways. This integrative network analysis approach revealed that gene expression and methylation consistently targeted the same gene pathways relevant to cancer: Pathways in cancer, Ras signaling pathway, PI3K-Akt signaling pathway, and Rap1 signaling pathway, among others. Detected gene hubs and hub sub-networks were integrated by signature loci associated with cancer that include, for example, NOTCH1, RAC1, PIK3CD, BCL2, and EGFR. Statistical analysis disclosed a stochastic deterministic relationship between methylation and gene expression within the set of genes simultaneously identified as DEGs and DMGs, where larger values of gene expression changes were probabilistically associated with larger values of methylation changes. Concordance analysis of the overlap between enriched pathways in DEG and DMG datasets revealed statistically significant agreement between gene expression and methylation changes. These results support the potential identification of reliable and stable methylation biomarkers at genes for cancer diagnosis and prognosis.
Highlights
Our study investigates protein-protein interaction networks (PPI), which are exclusively focused on protein-protein associations and resulting cell activities
We report on integrative network analysis of differentially methylated genes (DMGs), differentially expressed genes (DEGs) and DEG-DMGs, where the identification of DMGs was accomplished with the application of a novel signal detection-machine learning approach, Methyl-IT14, on whole genome bisulfite sequence (WGBS) data
Aberrant DNA methylation of key genes was reported in Acute Lymphoblastic Leukemia (ALL)[15], and we have tested a reproducible approach to integrating network analysis of DMGs, DEGs and DEG-DMGs within datasets from patients with pediatric ALL (PALL)
Summary
Our study investigates protein-protein interaction networks (PPI), which are exclusively focused on protein-protein associations and resulting cell activities. Integrative network analysis of cytosine DNA methylation and gene expression data in patients with cancer has resulted in several published reports[9,10,11,12,13], typically with data from The Cancer Genome Atlas (TCGA). Aberrant DNA methylation of key genes was reported in Acute Lymphoblastic Leukemia (ALL)[15], and we have tested a reproducible approach to integrating network analysis of DMGs, DEGs and DEG-DMGs within datasets from patients with pediatric ALL (PALL). This data integration may provide the basis for robust identification of reliable and stable biomarkers
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