The survival rate of patients with pancreatic cancer is low; therefore, continuous discovery and development of novel pancreatic cancer drugs are required. Functional network analysis is an integrated bioinformatics approach based on gene, target, and disease networks interaction, and it is extensively used in drug discovery and development.
This study aimed to identify if atenolol, a selective adrenergic inhibitor, can be repurposed for the treatment of pancreatic cancer using functional network analysis.
Direct target proteins (DTPs) and indirect target proteins (ITPs) were obtained from STITCH and STRING databases, respectively. Atenolol-mediated proteins (AMPs) were collected from DTPs and ITPs and further analyzed for gene ontology, KEGG pathway enrichment, genetic alterations, overall survival, and molecular docking.
We obtained 176 AMPs that consisted of 10 DTPs and 166 ITPs. Among the AMPs involved in the pancreatic cancer pathways, several AMPs such as MAPK1, RELA, MAPK8, STAT1, and STAT3 were identified. Genetic alterations in seven AMPs were identified in 0.9%-16% of patients. Patients with high mRNA levels of MAPK1, RELA, STAT3, GNB1, and MMP9 had significantly worse overall survival rates compared with patients with low expression. Molecular docking studies showed that RELA and MMP9 are potential target candidates of atenolol in the treatment of patients with pancreatic cancer.
In conclusion, atenolol can potentially be repurposed to target pancreatic cancer cells by modulating MMP9 and NF-κB signaling. The results of this study need to be further validated in vitro and in vivo.

References

PubMed