A systems-level integrative framework for genome-wide DNA methylation and gene expression data identifies differential gene expression modules under epigenetic control
Abstract
Motivation: There is a growing number of studies generating matched Illumina Infinium HumanMethylation450 and gene expression data, yet there is a corresponding shortage of statistical tools aimed at their integrative analysis. Such integrative tools are important for the discovery of epigenetically regulated gene modules or molecular pathways, which play key roles in cellular differentiation and disease. Results: Here, we present a novel functional supervised algorithm, called Functional Epigenetic Modules (FEM), for the integrative analysis of Infinium 450k DNA methylation and matched or unmatched gene expression data. The algorithm identifies gene modules of coordinated differential methylation and differential expression in the context of a human interactome. We validate the FEM algorithm on simulated and real data, demonstrating how it successfully retrieves an epigenetically deregulated gene, previously known to drive endometrial cancer development. Importantly, in the same cancer, FEM identified a novel epigenetically deregulated hotspot, directly upstream of the well-known progesterone receptor tumour suppressor pathway. In the context of cellular differentiation, FEM successfully identifies known endothelial cell subtype-specific gene expression markers, as well as a novel gene module whose overexpression in blood endothelial cells is mediated by DNA hypomethylation. The systems-level integrative framework presented here could be used to identify novel key genes or signalling pathways, which drive cellular differentiation or disease through an underlying epigenetic mechanism. Availability and implementation: FEM is freely available as an R-package from http://sourceforge.net/projects/funepimod. Contact: andrew@picb.ac.cn Supplementary information: Supplementary Data are available at Bioinformatics online.
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A network-interaction-based approach identifies one or more sets of genes (modules) that have coordinated differential expression and differential methylation.
“This approach identifies one or more sets of genes (also called modules) that have coordinated differential expression and differential methylation [145].”
From A survey of best practices for RNA-seq data analysis (2016) · cites this paper as [145]
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- Authors
- Jiao, Y., Widschwendter, M., & Teschendorff, A. E.
- Affiliations
- Chinese Academy of Sciences (CN)
- Corresponding author
- Teschendorff, Andrew E.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-05-02
- Volume
- 30
- Issue
- 16
- Pages
- 2360-2366
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2119507177
- Cited by
- 148
- References
- 22
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 4 · 2023: 9 · 2022: 10 · 2021: 18 · 2020: 21 · 2019: 22 · 2018: 13 · 2017: 24 · 2016: 8 · 2015: 10 · 2014: 3
- FWCI
- 6.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Chinese Academy of Sciences
- Max-Planck-Gesellschaft
- Directorate for Biological Sciences
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- DNA methylation, gene expression, epigenetic regulation, gene modules, integrative analysis, human interactome, endometrial cancer, endothelial cells, cell differentiation, DNA hypomethylation
- MeSH terms
- Algorithms, Cell Differentiation, Female, Humans, Genome, Human, Gene Expression Regulation, Neoplastic, Endometrial Neoplasms, DNA Methylation, Gene Expression Profiling, Transcription Initiation Site, Protein Interaction Mapping, Epigenesis, Genetic, Gene Regulatory Networks
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