ArticleScientific reports2016
Driver gene classification reveals a substantial overrepresentation of tumor suppressors among very large chromatin-regulating proteins.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 37 citations in OpenAlex.
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- Identification of a Five-Gene Panel to Assess Prognosis for Gastric Cancer.BioMed research international · 2022Article
- Current cancer driver variant predictors learn to recognize driver genes instead of functional variants.BMC biology · 2021Article
- Performance Assessment of the Network Reconstruction Approaches on Various Interactomes.Frontiers in molecular biosciences · 2021Article
- DORGE: Discovery of Oncogenes and tumoR suppressor genes using Genetic and Epigenetic features.Science advances · 2020Article
- Directional association test reveals high-quality putative cancer driver biomarkers including noncoding RNAs.BMC medical genomics · 2019Article
- DNA Repair Gene Expression Adjusted by theCancers · 2019Article
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- Mutation pattern analysis reveals polygenic mini-drivers associated with relapse after surgery in lung adenocarcinoma.Scientific reports · 2018Article
- Cancer-mutation network and the number and specificity of driver mutations.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Evaluation of deep learning techniques for identification of sarcoma-causing carcinogenic mutations.Digital healthArticle
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Compiling a comprehensive list of cancer driver genes is imperative for oncology diagnostics and drug development. While driver genes are typically discovered by analysis of tumor genomes, infrequently mutated driver genes often evade detection due to limited sample sizes. Here, we address sample size limitations by integrating tumor genomics data with a wide spectrum of gene-specific properties to search for rare drivers, functionally classify them, and detect features characteristic of driver genes. We show that our approach, CAnceR geNe similarity-based Annotator and Finder (CARNAF), enables detection of potentially novel drivers that eluded over a dozen pan-cancer/multi-tumor type studies. In particular, feature analysis reveals a highly concentrated pool of known and putative tumor suppressors among the <1% of genes that encode very large, chromatin-regulating proteins. Thus, our study highlights the need for deeper characterization of very large, epigenetic regulators in the context of cancer causality.
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