ArticleOncology reports2019
Novel drug candidate for the treatment of several soft‑tissue sarcoma histologic subtypes: A computational method using survival‑associated gene signatures for drug repurposing.
Article in Oncology reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- Article
- Comprehensive analysis of a novel cuproptosis-related lncRNA signature associated with prognosis and tumor matrix features to predict immunotherapy in soft tissue carcinoma.Frontiers in genetics · 2022Article
- Myogenic dedifferentiation is associated with poor outcomes in retroperitoneal dedifferentiated liposarcomas.Rare tumors · 2021Article
- Improving the odds of success in antitumoral drug development using scoring approaches towards heterocyclic scaffolds.Oncology reports · 2020Article
- Niclosamide inhibits the cell proliferation and enhances the responsiveness of esophageal cancer cells to chemotherapeutic agents.Oncology reports · 2020Article
- Influence of batch effect correction methods on drug induced differential gene expression profiles.BMC bioinformatics · 2019Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Systemic treatment options for soft tissue sarcomas (STSs) have remained unchanged despite the need for novel drug candidates to improve STS outcomes. Drug repurposing involves the application of clinical drugs to different diseases, reducing development time, and cost. It has also become a fast and effective way to identify drug candidates. The present study used a computational method to screen three drug‑gene interaction databases for novel drug candidates for the treatment of several common STS histologic subtypes through drug repurposing. STS survival‑associated genes were generated by conducting a univariate cox regression analysis using The Cancer Genome Atlas survival data. These genes were then applied to three databases (the Connectivity Map, the Drug Gene Interaction Database and the L1000 Fireworks Display) to identify drug candidates for STS treatment. Additionally, pathway analysis and molecular docking were conducted to evaluate the molecular mechanisms of the candidate drug. Bepridil was identified as a potential candidate for several STS histologic subtype treatments by overlapping the screening results from three drug‑gene interaction databases. The pathway analysis with the Kyoto Encyclopedia of Genes and Genomes predicted that Bepridil may target CRK, fibroblast growth factor receptor 4 (FGFR4), laminin subunit β1 (LAMB1), phosphoinositide‑3‑kinase regulatory subunit 2 (PIK3R2), WNT5A, cluster of differentiation 47 (CD47), elastase, neutrophil expressed (ELANE), 15‑hydroxyprostaglandin dehydrogenase (HPGD) and protein kinase cβ (PRKCB) to suppress STS development. Further molecular docking simulation suggested a relatively stable binding selectivity between Bepridil and eight proteins (CRK, FGFR4, LAMB1, PIK3R2, CD47, ELANE, HPGD, and PRKCB). In conclusion, a computational method was used to identify Bepridil as a potential candidate for the treatment of several common STS histologic subtypes. Experimental validation of these in silico results is necessary before clinical translation can occur.
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