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Novel drug candidate for the treatment of several soft?tissue sarcoma histologic subtypes: A computational method using survival?associated gene signatures for drug repurposing.

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 ?1 (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 c? (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.

SUBMITTER: Yang X

PROVIDER: S-EPMC6412453 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Novel drug candidate for the treatment of several soft‑tissue sarcoma histologic subtypes: A computational method using survival‑associated gene signatures for drug repurposing.

Yang Xia X Huang Wen-Ting WT Wu Hua-Yu HY He Rong-Quan RQ Ma Jie J Liu An-Gui AG Chen Gang G

Oncology reports 20190226 4

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 ...[more]

PMID: 30816547

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Dataset Information

Novel drug candidate for the treatment of several soft?tissue sarcoma histologic subtypes: A computational method using survival?associated gene signatures for drug repurposing.

Publications

Novel drug candidate for the treatment of several soft‑tissue sarcoma histologic subtypes: A computational method using survival‑associated gene signatures for drug repurposing.

Similar Datasets

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