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 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. Studies of protein-protein interaction (PPI) networks of differentially methylated (DMGs) and expressed genes (DEGs) showed that gene expression and methylation consistently targeted the same gene pathways associated with cancer: Pathways in cancer, Ras signaling pathway, PI3K-Akt signaling pathway, and Rap1 signaling pathway, among others. Detected gene hubs and hub sub-networks are integrated by signature loci associated with cancer that include, for example, NOTCH1, RAC1, PIK3CD, BCL2, and EGFR. Statistical analysis disclosed a stochastic deterministic dependence between methylation and gene expression within the set of genes simultaneously identified as DEGs and DMGs, where larger values of gene expression changes are 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, reflecting a coordinated response of methylation and gene-expression regulatory systems. These results support the identification of reliable and stable biomarkers for cancer diagnosis and prognosis.