ArticleNPJ genomic medicine2019
Integrative analysis with expanded DNA methylation data reveals common key regulators and pathways in cancers.
Article in NPJ genomic medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 49 citations in OpenAlex.
- The impact of epigenetics on tumor metabolism: Friend or foe in drug response?Frontiers in epigenetics and epigenomics · 2026Review
- Methylation-based signature to distinguish indolent and aggressive prostate cancer.Biology open · 2025Article
- DNA methylation and machine learning: challenges and perspective toward enhanced clinical diagnostics.Clinical epigenetics · 2025Review
- The EBV-Positive Tumor Methylome Is Distinct from EBV-Negative in Diffuse Large B-Cell Lymphoma.Cancers · 2025Article
- Article
- A clustering approach to integrative analyses of multiomic cancer data.Journal of applied statistics · 2025Article
- Multi-omics analysis reveals critical metabolic regulators in bladder cancer.International urology and nephrology · 2024Article
- Integrative multi-omic cancer profiling reveals DNA methylation patterns associated with therapeutic vulnerability and cell-of-origin.Cancer cell · 2023Article
- Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers.Medical sciences (Basel, Switzerland) · 2023Article
- Review
- LINE-1 promotes tumorigenicity and exacerbates tumor progression via stimulating metabolism reprogramming in non-small cell lung cancer.Molecular cancer · 2022Article
- Genome-wide DNA methylation profiling and identification of potential pan-cancer and tumor-specific biomarkers.Molecular oncology · 2022Article
- Epigenome-Wide DNA Methylation Profiling in Colorectal Cancer and Normal Adjacent Colon Using Infinium Human Methylation 450K.Diagnostics (Basel, Switzerland) · 2022Article
- Screening and Validation of Significant Genes with Poor Prognosis in Pathologic Stage-I Lung Adenocarcinoma.Journal of oncology · 2022Article
- Identification and Validation of a Prognostic Model Based on Three MVI-Related Genes in Hepatocellular Carcinoma.International journal of biological sciences · 2022Article
- Article
- Robust biomarker discovery for hepatocellular carcinoma from high-throughput data by multiple feature selection methods.BMC medical genomics · 2021Article
- Genome wide methylation profiling of selected matched soft tissue sarcomas identifies methylation changes in metastatic and recurrent disease.Scientific reports · 2021Article
- Crosstalk Between Inflammatory Signaling and Methylation in Cancer.Frontiers in cell and developmental biology · 2021Review
- PretiMeth: precise prediction models for DNA methylation based on single methylation mark.BMC genomics · 2020Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.