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NPJ Genom Med. 2019 Feb 1;4:2. doi: 10.1038/s41525-019-0077-8. eCollection 2019.

Integrative analysis with expanded DNA methylation data reveals common key regulators and pathways in cancers.

Fan S1,2,3,4, Tang J1, Li N3, Zhao Y3, Ai R3, Zhang K3, Wang M3, Du W4, Wang W3,5.

Author information

1
1School of Automation Engineering, University of Electronic Science and Technology of China, 611731 Chengdu, Sichuan China.
2
2Center for Informational Biology, University of Electronic Science and Technology of China, 611731 Chengdu, Sichuan China.
3
3Department of Chemistry and Biochemistry, University of California, San Diego, CA 92093-0359 USA.
4
4Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, 130012 Changchun, China.
5
5Department of Cellular and Molecular Medicine, University of California, San Diego, CA 92093-0359 USA.

Abstract

The integration of genomic and DNA methylation data has been demonstrated as a powerful strategy in understanding cancer mechanisms and identifying therapeutic targets. The TCGA consortium has mapped DNA methylation in thousands of cancer samples using Illumina Infinium Human Methylation 450 K BeadChip (Illumina 450 K array) that only covers about 1.5% of CpGs in the human genome. Therefore, increasing the coverage of the DNA methylome would significantly leverage the usage of the TCGA data. Here, we present a new model called EAGLING that can expand the Illumina 450 K array data 18 times to cover about 30% of the CpGs in the human genome. We applied it to analyze 13 cancers in TCGA. By integrating the expanded methylation, gene expression, and somatic mutation data, we identified the genes showing differential patterns in each of the 13 cancers. Many of the triple-evidenced genes identified in majority of the cancers are biomarkers or potential biomarkers. Pan-cancer analysis also revealed the pathways in which the triple-evidenced genes are enriched, which include well known ones as well as new ones, such as axonal guidance signaling pathway and pathways related to inflammatory processing or inflammation response. Triple-evidenced genes, particularly TNXB, RRM2, CELSR3, SLC16A3, FANCI, MMP9, MMP11, SIK1, and TRIM59 showed superior predictive power in both tumor diagnosis and prognosis. These results have demonstrated that the integrative analysis using the expanded methylation data is powerful in identifying critical genes/pathways that may serve as new therapeutic targets.

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