ArticleBMC genomics2015
Bioinformatics analysis of thousands of TCGA tumors to determine the involvement of epigenetic regulators in human cancer.
Article in BMC genomics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 41 citations in OpenAlex.
- CASER: A semi-supervised model with multi-omics data integration prioritizes cancer-associated epigenetic regulator genes.PLoS computational biology · 2026Article
- Mapping epigenetic gene variant dynamics: comparative analysis of frequency, functional impact and trait associations in African and European populations.Scientific reports · 2026Article
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- ACTL6A depletion induces KLF4-mediated anti-tumorigenic effects in colorectal cancer.Cell death & disease · 2025Article
- Histone H3E50K remodels chromatin to confer oncogenic activity and support an EMT phenotype.NAR cancer · 2025Article
- Proteomic characterization of post-translational modifications in drug discovery.Acta pharmacologica Sinica · 2022Review
- Network Pharmacology Approach for Medicinal Plants: Review and Assessment.Pharmaceuticals (Basel, Switzerland) · 2022Review
- Applying graph database technology for analyzing perturbed co-expression networks in cancer.Database : the journal of biological databases and curation · 2020Article
- EZH2 overexpression dampens tumor-suppressive signals via an EGR1 silencer to drive breast tumorigenesis.Oncogene · 2020Article
- DORGE: Discovery of Oncogenes and tumoR suppressor genes using Genetic and Epigenetic features.Science advances · 2020Article
- Systematic analysis of the intersection of disease mutations with protein modifications.BMC medical genomics · 2019Article
- Epigenomic Reordering Induced by Polycomb Loss Drives Oncogenesis but Leads to Therapeutic Vulnerabilities in Malignant Peripheral Nerve Sheath Tumors.Cancer research · 2019Article
- The Case for Proteomics and Phospho-Proteomics in Personalized Cancer Medicine.Proteomics. Clinical applications · 2019Review
- Genomic characterization of genes encoding histone acetylation modulator proteins identifies therapeutic targets for cancer treatment.Nature communications · 2019Article
- 15 years of PhosphoSitePlus®: integrating post-translationally modified sites, disease variants and isoforms.Nucleic acids research · 2019Article
- Comprehensive molecular biomarker identification in breast cancer brain metastases.Journal of translational medicine · 2017Article
- Epigenetic Alterations in Bone and Soft Tissue Tumors.Advances in anatomic pathology · 2017Review
- Genetic Variants in Epigenetic Pathways and Risks of Multiple Cancers in the GAME-ON Consortium.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2017Article
- The epigenomic basis of common diseases.Clinical epigenetics · 2017Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMany cancer cells show distorted epigenetic landscapes. The Cancer Genome Atlas (TCGA) project profiles thousands of tumors, allowing the discovery of somatic alterations in the epigenetic machinery and the identification of potential cancer drivers among members of epigenetic protein families.
methodsWe integrated mutation, expression, and copy number data from 5943 tumors from 13 cancer types to train a classification model that predicts the likelihood of being an oncogene (OG), tumor suppressor (TSG) or neutral gene (NG). We applied this predictor to epigenetic regulator genes (ERGs), and used differential expression and correlation network analysis to identify dysregulated ERGs along with co-expressed cancer genes. Furthermore, we quantified global proteomic changes by mass spectrometry after EZH2 inhibition.
resultsMutation-based classifiers uncovered the OG-like profile of DNMT3A and TSG-like profiles for several ERGs. Differential gene expression and correlation network analyses revealed that EZH2 is the most significantly over-expressed ERG in cancer and is co-regulated with a cell cycle network. Proteomic analysis showed that EZH2 inhibition induced down-regulation of cell cycle regulators in lymphoma cells.
conclusionsUsing classical driver genes to train an OG/TSG predictor, we determined the most predictive features at the gene level. Our predictor uncovered one OG and several TSGs among ERGs. Expression analyses elucidated multiple dysregulated ERGs including EZH2 as member of a co-expressed cell cycle network.
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