Department of Biology, The Pennsylvania State University, University Park, PA, 16802, USA.
Department of Plant Science, The Pennsylvania State University, University Park, PA, 16802, USA.
Sci Rep. 2020 Feb 7;10(1):2123. doi: 10.1038/s41598-020-58123-2.
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.
全基因组 DNA 甲基化和基因表达在小儿急性淋巴细胞白血病（PALL）中通常会发生改变。对胞嘧啶甲基化和表达数据集进行综合网络分析，有可能比单独进行离散分析提供更深入地了解复杂的疾病状态及其原因。为了从白血病患者的基因区域中确定可靠的癌症相关甲基化信号，我们对差异甲基化（DMGs）和差异表达基因（DEGs）进行了综合网络分析。一种新颖的信号检测-机器学习方法在全基因组亚硫酸氢盐测序（WGBS）数据分析中的应用，使癌症相关基因和途径中的甲基化信号分辨率达到了很高的水平。这种综合网络分析方法表明，基因表达和甲基化一致地靶向与癌症相关的相同基因途径：癌症途径、Ras 信号通路、PI3K-Akt 信号通路和 Rap1 信号通路等。检测到的基因枢纽和枢纽子网通过与癌症相关的特征基因座进行整合，例如 NOTCH1、RAC1、PIK3CD、BCL2 和 EGFR。在同时被鉴定为 DEG 和 DMG 的基因集中，对甲基化和基因表达之间的随机确定性关系进行了统计分析，其中基因表达变化的较大值与甲基化变化的较大值具有概率相关性。DEG 和 DMG 数据集富集途径之间的重叠一致性分析表明，基因表达和甲基化变化之间存在统计学显著的一致性。这些结果支持在癌症诊断和预后中鉴定可靠和稳定的基因甲基化生物标志物的潜力。
