ArticleOncotarget2016
Ranking novel cancer driving synthetic lethal gene pairs using TCGA data.
Article in Oncotarget, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
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- Multi-level characteristics recognition of cancer core therapeutic targets and drug screening for a broader patient population.Frontiers in pharmacology · 2023Article
- SLOAD: a comprehensive database of cancer-specific synthetic lethal interactions for precision cancer therapy via multi-omics analysis.Database : the journal of biological databases and curation · 2022Article
- SynLethDB 2.0: a web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery.Database : the journal of biological databases and curation · 2022Article
- Computational methods, databases and tools for synthetic lethality prediction.Briefings in bioinformatics · 2022Review
- Functional buffering via cell-specific gene expression promotes tissue homeostasis and cancer robustness.Scientific reports · 2022Article
- Bioinformatic analysis linking genomic defects to chemosensitivity and mechanism of action.PloS one · 2021Article
- Mapping the landscape of synthetic lethal interactions in liver cancer.Theranostics · 2021Article
- Therapeutic targeting of the DNA damage response in prostate cancer.Current opinion in oncology · 2020Article
- Fanconi anemia pathway as a prospective target for cancer intervention.Cell & bioscience · 2020Review
- Genetic Interaction-Based Biomarkers Identification for Drug Resistance and Sensitivity in Cancer Cells.Molecular therapy. Nucleic acids · 2019Article
- Article
- A Multidimensional Characterization of E3 Ubiquitin Ligase and Substrate Interaction Network.iScience · 2019Article
- Synthetic Lethality-based Identification of Targets for Anticancer Drugs in the Human Signaling Network.Scientific reports · 2018Article
- Candidate synthetic lethality partners to PARP inhibitors in the treatment of ovarian clear cell cancer.Biomedical reports · 2017Article
- Exploiting the Ref-1-APE1 node in cancer signaling and other diseases: from bench to clinic.NPJ precision oncology · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Synthetic lethality (SL) has emerged as a promising approach to cancer therapy. In contrast to the costly and labour-intensive genome-wide siRNA or CRISPR-based human cell line screening approaches, computational approaches to prioritize potential synthetic lethality pairs for further experimental validation represent an attractive alternative. In this study, we propose an efficient and comprehensive in-silico pipeline to rank novel SL gene pairs by mining vast amounts of accumulated tumor high-throughput sequencing data in The Cancer Genome Atlas (TCGA), coupled with other protein interaction networks and cell line information. Our pipeline integrates three significant features, including mutation coverage in TCGA, driver mutation probability and the quantified cancer network information centrality, into a ranking model for SL gene pair identification, which is presented as the first learning-based method for SL identification. As a result, 107 potential SL gene pairs were obtained from the top 10 results covering 11 cancers. Functional analysis of these genes indicated that several promising pathways were identified, including the DNA repair related Fanconi Anemia pathway and HIF-1 signaling pathway. In addition, 4 SL pairs, mTOR-TP53, VEGFR2-TP53, EGFR-TP53, ATM-PRKCA, were validated using drug sensitivity information in the cancer cell line databases CCLE or NCI60. Interestingly, significant differences in the cell growth of mTOR siRNA or EGFR siRNA knock-down were detected between cancer cells with wild type TP53 and mutant TP53. Our study indicates that the pre-screening of potential SL gene pairs based on the large genomics data repertoire of tumor tissues and cancer cell lines could substantially expedite the identification of synthetic lethal gene pairs for cancer therapy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.