ArticleOncotarget2016
RWCFusion: identifying phenotype-specific cancer driver gene fusions based on fusion pair random walk scoring method.
Article in Oncotarget, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Article
- Enhancing Molecular Network-Based Cancer Driver Gene Prediction Using Machine Learning Approaches: Current Challenges and Opportunities.Journal of cellular and molecular medicine · 2025Review
- Aberrant expression of collagen type X in solid tumor stroma is associated with EMT, immunosuppressive and pro-metastatic pathways, bone marrow stromal cell signatures, and poor survival prognosis.bioRxiv : the preprint server for biology · 2024Article
- Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods.Genome biology · 2019Article
- Predicting links between tumor samples and genes using 2-Layered graph based diffusion approach.BMC bioinformatics · 2019Article
- Synstable Fusion: A Network-Based Algorithm for Estimating Driver Genes in Fusion Structures.Molecules (Basel, Switzerland) · 2018Article
- Identification and validation of potential prognostic gene biomarkers for predicting survival in patients with acute myeloid leukemia.OncoTargets and therapy · 2017Article
Corrections and comments
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Authors and funding
16 authors.
Funding
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Abstract
While gene fusions have been increasingly detected by next-generation sequencing (NGS) technologies based methods in human cancers, these methods have limitations in identifying driver fusions. In addition, the existing methods to identify driver gene fusions ignored the specificity among different cancers or only considered their local rather than global topology features in networks. Here, we proposed a novel network-based method, called RWCFusion, to identify phenotype-specific cancer driver gene fusions. To evaluate its performance, we used leave-one-out cross-validation in 35 cancers and achieved a high AUC value 0.925 for overall cancers and an average 0.929 for signal cancer. Furthermore, we classified 35 cancers into two classes: haematological and solid, of which the haematological got a highly AUC which is up to 0.968. Finally, we applied RWCFusion to breast cancer and found that top 13 gene fusions, such as BCAS3-BCAS4, NOTCH-NUP214, MED13-BCAS3 and CARM-SMARCA4, have been previously proved to be drivers for breast cancer. Additionally, 8 among the top 10 of the remaining candidate gene fusions, such as SULF2-ZNF217, MED1-ACSF2, and ACACA-STAC2, were inferred to be potential driver gene fusions of breast cancer by us.
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