Dataset Information

Identification of epigenetic modulators in human breast cancer by integrated analysis of DNA methylation and RNA-Seq data.

ABSTRACT: Human tumors undergo massive changes in DNA methylation. Recent studies showed that site-specific methylation of CpG sites is determined by the DNA sequence context surrounding the CpG site, which alludes to a possible mechanism for site-specific aberrant DNA methylation in cancer through DNA-binding proteins. In this paper, DNA methylation data and RNA-Seq data of breast tumors and normal tissues in the database of The Cancer Genome Atlas (TCGA) were integrated with information of DNA motifs in seven databases to find DNA-binding proteins and their binding motifs that were involved in aberrant DNA methylation in breast cancer. A total of 42,850 differentially methylated regions (DMRs) that include 77,298 CpG sites were detected in breast cancer. One hundred eight DNA motifs were found to be enriched in DMRs, and 109 genes encoding proteins binding to these motifs were determined. Based on these motifs and genes, 63 methylation modulator genes were identified to regulate differentially methylated CpG sites in breast cancer. A network of these 63 modulator genes and 645 transcription factors was constructed, and 20 network modules were determined. A number of pathways and gene sets related to breast cancer were found to be enriched in these network modules. The 63 methylation modulator genes identified may play an important role in aberrant methylation of CpG sites in breast cancer. They may help to understand site-specific dysregulation of DNA methylation and provide epigenetic markers for breast cancer.

SUBMITTER: Zhou X

PROVIDER: S-EPMC6291302 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Identification of epigenetic modulators in human breast cancer by integrated analysis of DNA methylation and RNA-Seq data.

Zhou Xin X Chen Zhibin Z Cai Xiaodong X

Epigenetics 20180807 5

Human tumors undergo massive changes in DNA methylation. Recent studies showed that site-specific methylation of CpG sites is determined by the DNA sequence context surrounding the CpG site, which alludes to a possible mechanism for site-specific aberrant DNA methylation in cancer through DNA-binding proteins. In this paper, DNA methylation data and RNA-Seq data of breast tumors and normal tissues in the database of The Cancer Genome Atlas (TCGA) were integrated with information of DNA motifs in ...[more]

PMID: 29940789

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Project description:ObjectiveTo identify novel DNA methylation-regulated differentially expressed genes (MeDEGs) in RA by integrated analysis of DNA methylation and RNA-Seq data.MethodsThe transcription and DNA methylation profiles of 9 RA and 15 OA synovial tissue were generated by RNA-Seq and Illumina 850K DNA methylation BeadChip. Gene set enrichment analysis (GSEA) and Weighted gene co-expression network analysis (WGCNA) were used to analyze methylation-regulated expressed genes by R software. The differentially expressed genes (DEGs), differentially methylated probes (DMPs), differentially methylated genes (DMGs) were analyzed by DESeq and ChAMP R package. The functional correlation of MeDEGs was analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The protein-protein interaction (PPI) network of MeDEGs was constructed by STRING and Reactome FI Cytoscape Plugin. Correlation analysis between methylation level and mRNA expression was conducted with R software.ResultsA total of 17,736 genes, 25,578 methylated genes and 755,852 methylation probes were detected. A total of 16,421 methylation-regulated expressed genes were obtained. The GSEA showed that these genes are associated with activation of immune response, adaptive immune response, Inflammatory response in C5 (ontology gene sets). For KEGG analysis, these genes are associated with rheumatoid arthritis, NF-kappa B signaling pathway, T cell receptor signaling pathway. The WGCNA showed that the turquoise module exhibited the strongest correlation with RA (R = 0.78, P = 1.27 × 10- 05), 660 genes were screened in the turquoise module. A total of 707 MeDEGs were obtained. GO analysis showed that MeDEGs were enriched in signal transduction, cell adhesion for BP, enriched in plasma membrane, integral component of membrane for CC, and enriched in identical protein binding, calcium ion binding for MF. The KEGG pathway analysis showed that the MeDEGs were enriched in calcium signaling pathway, T cell receptor signaling pathway, NF-kappa B signaling pathway, Rheumatoid arthritis. The PPI network containing 706 nodes and 882 edges, and the enrichment p value < 1.0 × 10- 16. With Cytoscape, based on the range of more than 10 genes, a total of 8 modules were screened out. Spearman correlation analysis showed RGS1(cg10718027), RGS1(cg02586212), RGS1(cg10861751) were significantly correlated with RA.ConclusionsRGS1 can be used as novel methylated biomarkers for RA.

Dataset Information

Identification of epigenetic modulators in human breast cancer by integrated analysis of DNA methylation and RNA-Seq data.

Publications

Identification of epigenetic modulators in human breast cancer by integrated analysis of DNA methylation and RNA-Seq data.

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