ArticleScientific reports2016
Systematic analysis of mutation distribution in three dimensional protein structures identifies cancer driver genes.
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 17 papers.
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Who cites it
17 citing papers in PubMed, 23 citations in OpenAlex.
- Oncodrive3D: fast and accurate detection of structural clusters of somatic mutations under positive selection.Nucleic acids research · 2025Article
- Mut-Map: Comprehensive Computational Pipeline for Structural Mapping and Analysis of Cancer-Associated Mutations.Briefings in bioinformatics · 2024Article
- Role of RNA Splicing Mutations in Diffuse Large B Cell Lymphoma.International journal of general medicine · 2023Review
- Faltwerk: a library for spatial exploratory data analysis of protein structures.Bioinformatics advances · 2023Article
- A New View of Activating Mutations in Cancer.Cancer research · 2022Article
- HNF1A POU Domain Mutations Found in Japanese Liver Cancer Patients Cause Downregulation ofGenes · 2022Article
- Novel ratio-metric features enable the identification of new driver genes across cancer types.Scientific reports · 2022Article
- Enhanced interpretation of 935 hotspot and non-hotspot RAS variants using evidence-based structural bioinformatics.Computational and structural biotechnology journal · 2022Article
- eVIDENCE: a practical variant filtering for low-frequency variants detection in cell-free DNA.Scientific reports · 2019Article
- DriverML: a machine learning algorithm for identifying driver genes in cancer sequencing studies.Nucleic acids research · 2019Article
- Functional characterization of 3D protein structures informed by human genetic diversity.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Clinical utility of androgen receptor gene aberrations in circulating cell-free DNA as a biomarker for treatment of castration-resistant prostate cancer.Scientific reports · 2019Article
- A CATH domain functional family based approach to identify putative cancer driver genes and driver mutations.Scientific reports · 2019Article
- Defining HIV-1 Envelope N-Glycan Microdomains through Site-Specific Heterogeneity Profiles.Journal of virology · 2019Article
- Loss-of-function mutations in Zn-finger DNA-binding domain ofOncotarget · 2018Article
- Structural dynamics is a determinant of the functional significance of missense variants.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Survival associated alternative splicing events in diffuse large B-cell lymphoma.American journal of translational research · 2018Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein tertiary structure determines molecular function, interaction, and stability of the protein, therefore distribution of mutation in the tertiary structure can facilitate the identification of new driver genes in cancer. To analyze mutation distribution in protein tertiary structures, we applied a novel three dimensional permutation test to the mutation positions. We analyzed somatic mutation datasets of 21 types of cancers obtained from exome sequencing conducted by the TCGA project. Of the 3,622 genes that had ≥3 mutations in the regions with tertiary structure data, 106 genes showed significant skew in mutation distribution. Known tumor suppressors and oncogenes were significantly enriched in these identified cancer gene sets. Physical distances between mutations in known oncogenes were significantly smaller than those of tumor suppressors. Twenty-three genes were detected in multiple cancers. Candidate genes with significant skew of the 3D mutation distribution included kinases (MAPK1, EPHA5, ERBB3, and ERBB4), an apoptosis related gene (APP), an RNA splicing factor (SF1), a miRNA processing factor (DICER1), an E3 ubiquitin ligase (CUL1) and transcription factors (KLF5 and EEF1B2). Our study suggests that systematic analysis of mutation distribution in the tertiary protein structure can help identify cancer driver genes.
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