Integrative Network Analysis of Differentially Methylated and Expressed Genes for Biomarker Identification in Leukemia

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.Network-based modeling approaches have the potential to integrate and improve the perception of complex disease states and their root causes. To date, network analysis provides reliable and cost effective approaches for early disease detection, prediction of co-occurring diseases and interactions, and drug design 1 . Although integrated genomic analysis of methylation and gene expression in leukemia has been reported 2-5 , meaningful assimilation of network analysis is still lacking.Our study investigates protein-protein interaction networks (PPI), which are exclusively focused on protein-protein associations and resulting cell activities. A PPI network can be defined as a (un)directed graph/ network holding vertices as proteins (or protein-coding genes) and edges as the interactions/association between them. Associations are meant to be specific and biologically meaningful, i.e., two proteins are connected by an edge if jointly contributing to a shared function, which does not necessarily reflect a physical binding interaction.Within the network, some proteins denote hubs interacting with numerous partners. Biologically, hubs are key elements on which functionality of the cellular proces...

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