ArticleScience advances2020
DORGE: Discovery of Oncogenes and tumoR suppressor genes using Genetic and Epigenetic features.
Article in Science advances, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
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Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Uncovering Oncogenic Mechanisms of Tumor Suppressor Genes in Breast Cancer Multi-Omics Data.International journal of molecular sciences · 2022Pooled it
- Chromatin regulator expression patterns predict prognosis and guide neoadjuvant therapy in esophageal squamous cell carcinoma.Fundamental research · 2026Article
- TGBWDriver: A Cancer Driver Gene Identification Method Based on GraphSAGE and Bidirectional Weighted Feature Aggregation.International journal of molecular sciences · 2026Article
- CASER: A semi-supervised model with multi-omics data integration prioritizes cancer-associated epigenetic regulator genes.PLoS computational biology · 2026Article
- ONCOPLEX: an oncology-inspired hypergraph model integrating diverse biological knowledge for cancer driver gene prediction.Scientific reports · 2026Article
- MODCAN: driver gene identification based on multi-omics features and differential co-association networks for tumor subtypes.BMC bioinformatics · 2026Article
- ALG3 as a PanCancer Oncogene: Bioinformatics Analysis and Identification of Small-Molecule Inhibitors.Anti-cancer agents in medicinal chemistry · 2026Article
- The dysregulation score method identifies epigenetic regulator genes that predict cancer prognosis and efficiency of cancer immunotherapy.Molecular therapy. Nucleic acids · 2025Article
- Downregulation of TMTC4 expression is associated with improved prognosis in osteosarcoma.The Journal of international medical research · 2025Article
- CRISPR-StAR enables high-resolution genetic screening in complex in vivo models.Nature biotechnology · 2025Article
- Prediction of Proto-Oncogene Using Bidirectional GRU and Attention.Asian Pacific journal of cancer prevention : APJCP · 2025Article
- POCALI: Prediction and Insight on CAncer LncRNAs by Integrating Multi-Omics Data with Machine Learning.Small methods · 2025Article
- Enhancing Molecular Network-Based Cancer Driver Gene Prediction Using Machine Learning Approaches: Current Challenges and Opportunities.Journal of cellular and molecular medicine · 2025Review
- MONet: cancer driver gene identification algorithm based on integrated analysis of multi-omics data and network models.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- The evolution of cancer immunotherapy: a comprehensive review of its history and current perspectives.Korean journal of clinical oncology · 2024Review
- Deciphering gene expression patterns using large-scale transcriptomic data and its applications.Briefings in bioinformatics · 2024Article
- IMI-driver: Integrating multi-level gene networks and multi-omics for cancer driver gene identification.PLoS computational biology · 2024Article
- Identifying new cancer genes based on the integration of annotated gene sets via hypergraph neural networks.Bioinformatics (Oxford, England) · 2024Article
- Review
- A distinct class of pan-cancer susceptibility genes revealed by an alternative polyadenylation transcriptome-wide association study.Nature communications · 2024Article
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7 authors.
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Abstract
Data-driven discovery of cancer driver genes, including tumor suppressor genes (TSGs) and oncogenes (OGs), is imperative for cancer prevention, diagnosis, and treatment. Although epigenetic alterations are important for tumor initiation and progression, most known driver genes were identified based on genetic alterations alone. Here, we developed an algorithm, DORGE (Discovery of Oncogenes and tumor suppressoR genes using Genetic and Epigenetic features), to identify TSGs and OGs by integrating comprehensive genetic and epigenetic data. DORGE identified histone modifications as strong predictors for TSGs, and it found missense mutations, super enhancers, and methylation differences as strong predictors for OGs. We extensively validated DORGE-predicted cancer driver genes using independent functional genomics data. We also found that DORGE-predicted dual-functional genes (both TSGs and OGs) are enriched at hubs in protein-protein interaction and drug-gene networks. Overall, our study has deepened the understanding of epigenetic mechanisms in tumorigenesis and revealed previously undetected cancer driver genes.
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