Dataset Information

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.

SUBMITTER: Sanchez R

PROVIDER: S-EPMC7005804 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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

Sanchez Robersy R Mackenzie Sally A SA

Scientific reports 20200207 1

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 differen ...[more]

PMID: 32034170

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Project description:BackgroundAge-related macular degeneration (AMD) is the leading cause of blindness for people over 50 years old worldwide. The purpose of this study was to identify differentially expressed and methylated genes (DEMGs) and construct a co-expression network for AMD.MethodsMicroarray expression (GSE29801 dataset) and DNA methylation (GSE102952 dataset) profiles were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) were analyzed between AMD retina tissues and normal retina tissues. A protein-protein interaction (PPI) network was constructed and hub genes were screened, followed by functional enrichment analysis. Then, weighted gene co-expression network analysis (WGCNA) was conducted. The ARPE-19 cells were maintained in a hypoxic state to construct an AMD cellular model. Enzyme-linked immunosorbent assay (ELISA) and the real-time qPCR (RT-qPCR) were performed for validation.ResultsAfter overlapping, 16 hypermethylated and down-regulated genes and 15 hypomethylated and up-regulated genes were identified for extramacular AMD. A total of 4 hub genes (LMNB2, EMD, HLA-A, and HLA-B) were screened for AMD in the extramacular retina. Furthermore, 13 hypermethylated and down-regulated genes and 31 hypomethylated and up-regulated genes were identified for macular AMD. Among them, 11 hub genes (HLA-A, HLA-B, HLA-DRB1, IFITM3, SAT1, MAOB, CHRDL1, FSTL1, HSPA1A, AR, and YAP1) were considered hub genes. The DEMGs were distinctly related with immune-related biological processes and pathways. A total of 16 co-expression modules were constructed, of which 2 significantly correlated with AMD. The genes in the 2 modules were involved in various crucial signaling pathways. The HIF1α and VEGF levels were significantly up-regulated in cell supernatant of hypoxia-induced ARPE-19 cells, indicating that the AMD cellular model was successfully established. Hub genes including CHRDL, FSTL1, and IFITM3 displayed significantly higher expression in hypoxia-induced ARPE-19 cells compared to normal cells. Greater up-regulation of CHRDL, FSTL1, and IFITM3 expression was found in hypoxia-induced ARPE-19 cells than in normal cells.ConclusionsThese findings offered several key DEMGs and pathways for AMD and constructed AMD-related co-expression modules, deepening understanding of the pathogenesis of AMD.

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Dataset Information

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

Publications

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

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