Comprehensive exploration of JQ1 and GSK2801 targets in breast cancer using network pharmacology and molecular modeling approaches

被引:1
作者
Yellapu, Nanda Kumar [1 ]
Pei, Dong [1 ]
Nissen, Emily [1 ]
Thompson, Jeffrey A. [1 ]
Koestler, Devin C. [1 ]
机构
[1] Univ Kansas, Med Ctr, Dept Biostat & Data Sci, Kansas City, KS 66045 USA
关键词
Network pharmacology; Breast cancer; Protein-Protein Interaction network maps; Drug targets; BROMODOMAIN INHIBITOR; PSEN2; GENE; EXPRESSION; PATHWAY; CELLS; RECOGNITION; RESISTANCE; MECHANISM; PROTEINS;
D O I
10.1016/j.csbj.2023.06.003
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
JQ1 and GSK2801 are bromo domain inhibitors (BDI) known to exhibit enhanced anti-cancer activity when combined with other agents. However, the underlying molecular mechanisms behind such enhanced activity remain unclear. We used network-pharmacology approaches to understand the shared molecular mechanisms behind the enhanced activity of JQ1 and GSK2801 when used together to treat breast cancer (BC). The gene targets of JQ1 and GSK2801 were intersected with known BC-targets and their putative targets against BC were derived. The key genes were explored through gene-ontology-enrichment, ProteinProtein-Interaction (PPI) networking, survival analysis, and molecular modeling simulations. The genes, CTSB, MAPK14, MET, PSEN2 and STAT3, were found to be common targets for both drugs. In total, 49 biological processes, five molecular functions and 61 metabolic pathways were similarly enriched for JQ1 and GSK2801 BC targets among which several terms are related to cancer: IL-17, TNF and JAK-STAT signaling pathways. Survival analyses revealed that all five putative synergistic targets are significantly associated with survival in BC (log-rank p < 0.05). Molecular modeling studies showed stable binding of JQ1 and GSK2801 against their targets. In conclusion, this study explored and illuminated the possible molecular mechanisms behind the enhanced activity of JQ1 and GSK2801 against BC and suggests synergistic action through their similar BC-targets and gene-ontologies. & COPY; 2023 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
引用
收藏
页码:3224 / 3233
页数:10
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