ArticleOncotarget2016
OncoBinder facilitates interpretation of proteomic interaction data by capturing coactivation pairs in cancer.
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.
- Beyond canonical PROTAC: biological targeted protein degradation (bioTPD).Biomaterials research · 2023Review
- Article
- Integrative Analysis of Exosomal miR-452 and miR-4713 DownregulatingDisease markers · 2022Article
- PIK3CB is involved in metastasis through the regulation of cell adhesion to collagen I in pancreatic cancer.Journal of advanced research · 2021Article
- Small molecule inhibitors targeting the PD-1/PD-L1 signaling pathway.Acta pharmacologica Sinica · 2021Review
- HIP1R targets PD-L1 to lysosomal degradation to alter T cell-mediated cytotoxicity.Nature chemical biology · 2019Article
- The OncoPPi Portal: an integrative resource to explore and prioritize protein-protein interactions for cancer target discovery.Bioinformatics (Oxford, England) · 2018Article
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6 authors.
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Abstract
High-throughput methods such as co-immunoprecipitationmass spectrometry (coIP-MS) and yeast 2 hybridization (Y2H) have suggested a broad range of unannotated protein-protein interactions (PPIs), and interpretation of these PPIs remains a challenging task. The advancements in cancer genomic researches allow for the inference of "coactivation pairs" in cancer, which may facilitate the identification of PPIs involved in cancer. Here we present OncoBinder as a tool for the assessment of proteomic interaction data based on the functional synergy of oncoproteins in cancer. This decision tree-based method combines gene mutation, copy number and mRNA expression information to infer the functional status of protein-coding genes. We applied OncoBinder to evaluate the potential binders of EGFR and ERK2 proteins based on the gastric cancer dataset of The Cancer Genome Atlas (TCGA). As a result, OncoBinder identified high confidence interactions (annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) or validated by low-throughput assays) more efficiently than co-expression based method. Taken together, our results suggest that evaluation of gene functional synergy in cancer may facilitate the interpretation of proteomic interaction data. The OncoBinder toolbox for Matlab is freely accessible online.
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