Exploring the Mechanism of Sempervirine Inhibiting Glioblastoma Invasion Based on Network Pharmacology and Bioinformatics.

Bingqiang Zhang, Wenyi Wang, Yu Song, Huixian Chen, Xinxin Lin, Jingjing Chen, Ying Chen, Jinfang Huang, Desen Li, Shuisheng Wu
Author Information
  1. Bingqiang Zhang: College of Pharmacy, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China.
  2. Wenyi Wang: Innovation and Transformation Center, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China.
  3. Yu Song: College of Pharmacy, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China.
  4. Huixian Chen: College of Pharmacy, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China.
  5. Xinxin Lin: College of Pharmacy, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China.
  6. Jingjing Chen: College of Pharmacy, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China.
  7. Ying Chen: College of Pharmacy, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China.
  8. Jinfang Huang: Fuzhou First General Hospital, Fuzhou 350009, China.
  9. Desen Li: College of Pharmacy, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China.
  10. Shuisheng Wu: College of Pharmacy, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China.

Abstract

: Invasion is an important characteristic of the malignancy of glioblastoma (GBM) and a significant prognostic factor. Sempervirine (SPV), a yohimbine-type alkaloid, has been proven to inhibit GBM cells proliferation in previous research and found to have a potential effect in anti-invasion, but its mechanism of anti-invasion is still unknown. : To explore its pharmacodynamics in inhibiting GBM cell invasion in this study, we combined network pharmacology and bioinformatics to comprehensive exploratory analysis of SPV and verified the mechanism in vitro. : Firstly, targets of SPV and invasion-related genes were collected from public databases. Moreover, GBM samples were obtained to analyze differentially expressed genes (DEGs) from The Cancer Genome Atlas (TCGA). Then, the relevant targets of SPV inhibiting GBM invasion (SIGI) were obtained through the intersection of the three gene sets. Further, GO and KEGG analysis showed that the targets of SIGI were heavily enriched in the AKT signaling pathway. Subsequently, based on the method of machine learning, a clinical prognostic model of the relevant targets of SIGI was constructed using GBM samples from TCGA and the Gene Expression Omnibus (GEO). A four-genes model (, , , and ) was successfully constructed, and Vina Scores of MMP2 and MMP13 in molecular docking were higher, which may be the main targets of SIGI. Then, the effect of SIGI was confirmed via functional experiments on invasion, migration, and adhesion assay, and the effect involved changes in the expressions of p-AKT, MMP2 and MMP13. Finally, combined with AKT activator (SC79) and inhibitor (MK2206), we further confirmed that SPV inhibits GBM invasion through AKT phosphorylation. : This study provides valuable and an expected point of view into the regulation of AKT phosphorylation and inhibition of GBM invasion by SPV.

Keywords

References

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