Suppressing the progression of bladder cancer using cyclovirobuxine D based on network pharmacology and bioinformatics approaches

Naunyn Schmiedebergs Arch Pharmacol. 2025 Jan 11. doi: 10.1007/s00210-024-03754-9. Online ahead of print.

Abstract

Limited treatment options are available for bladder cancer (BCa) resulting in extremely high mortality rates. Cyclovirobuxine D (CVB-D), a naturally alkaloid, reportedly exhibits notable antitumor activity against diverse tumor types. However, its impact on CVB-D on BCa and its precise molecular targets remain unexplored. This study conducts CCK8 assay, colony formation assay, and flow cytometry experiments to demonstrate that CVB-D inhibits long-term proliferation and viability of BCa cell lines, thereby inducing apoptosis in vitro. It employs PPI networks and the CytoHubba algorithm to identify COL1A1, COL6A1, COL6A2, COL5A2, COL5A1, COL12A1, COL18A1, ITGA5, VCL, FLNA, and GSN as crucial therapeutic targets for CVB-D that can halt the malignant progression of BCa. GO and KEGG analyses indicate that the PI3K/AKT signaling pathway potentially may play a pivotal role in mediating the anti-BCa growth effects of CVB-D. The ROC curve and K-M survival analyses reveal the significant clinical value of all the 11 identified therapeutic targets, with GSN as the most effective target of CVB-D in combating BCa. This study also uncovers a potential interaction between GSN and CVB-D through molecular docking and molecular dynamics simulations. RT-qPCR and Western blotting experiments provide further evidence that CVB-D effectively suppresses GSN mRNA and protein expression in a concentration-dependent fashion. Our comprehensive study is the first report on the molecular mechanism of CVB-D against BCa, identifying GSN as a pivotal target in CVB-D-based anti-BCa therapy. We believe that our study results may help establish a theoretical basis for the possible utilization of CVB-D in cancer therapeutics.

Keywords: Bladder cancer; Cyclovirobuxine D; GSN; Molecular dynamics simulation; Weighted gene co-expression network analysis.