Glycosylation of FGFR4 in cholangiocarcinoma regulates receptor processing and cancer signaling

AbstractThe purpose of this study was to determine if a methanolic extract of the Pulsatilla patens (L.) Mill. can inhibit the progression of cancer through the modulation of cancer-related metabolic signaling pathways. We analyzed a panel of 13 inducible luciferase reporter gene vectors which expression is driven by enhancer elements that bind to specific transcription factors for the evaluation of the activity of cancer signaling pathways. The root extract of P. patens exhibited strong inhibition of several signaling pathways in HeLa cells, a cervical cancer cell line, and was found to be the most potent in inhibiting the activation of Stat3, Smad, AP-1, NF-κB, MYC, Ets, Wnt and Hdghog, at a concentration of 40 µg/mL. The methanolic extracts of P. patens enhanced apoptotic death, deregulated cellular proliferation, differentiation, and progression towards the neoplastic phenotype by altering key signaling molecules required for cell cycle progression. This is the first study to report the influence of Pulsatilla species on cancer signaling pathways. Further, our detailed phytochemical analysis of the methanolic extracts of the P. patens allowed to deduce that compounds, which strongly suppressed the growth and proliferation of HeLa cancer cells were mainly triterpenoid saponins accompanied by phenolic acids.

Download Full-text

Identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks

Scientific Reports ◽

10.1038/s41598-021-93336-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Tien-Dzung Tran ◽

Duc-Tinh Pham

Keyword(s):

Anticancer Drug ◽

Drug Target ◽

Target Genes ◽

Signaling Network ◽

Competitive Dynamics ◽

Signaling Networks ◽

Molecular Signaling ◽

Dynamics Model ◽

Cancer Signaling ◽

The Impact

AbstractEach cancer type has its own molecular signaling network. Analyzing the dynamics of molecular signaling networks can provide useful information for identifying drug target genes. In the present study, we consider an on-network dynamics model—the outside competitive dynamics model—wherein an inside leader and an opponent competitor outside the system have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. If any normal agent links to the external competitor, the state of each normal agent will converge to a stable value, indicating support to the leader against the impact of the competitor. We determined the total support of normal agents to each leader in various networks and observed that the total support correlates with hierarchical closeness, which identifies biomarker genes in a cancer signaling network. Of note, by experimenting on 17 cancer signaling networks from the KEGG database, we observed that 82% of the genes among the top 3 agents with the highest total support are anticancer drug target genes. This result outperforms those of four previous prediction methods of common cancer drug targets. Our study indicates that driver agents with high support from the other agents against the impact of the external opponent agent are most likely to be anticancer drug target genes.

Download Full-text