Exploring the potential mechanism of artemisinin against gastric cancer based on network pharmacology, machine learning and experimental validation
4 affiliations
- 1The First Affiliated Hospital of Shihezi University, National Health Commission (NHC) Key Laboratory of Prevention and Treatment of Central Asia High Incidence Diseases (Co-construction)
- 2Department of Gastrointestinal Surgery, The First Affiliated Hospital of Shihezi University
- 3Shihezi University School of Medicine
- 4Shihezi People’s Hospital (The Third Affiliated Hospital of Shihezi University School of Medicine)
Abstract
Background: Artemisinin, a natural active component extracted from Artemisia annua , exhibits potential anti-gastric cancer activity; however, its specific targets and molecular mechanisms remain unclear.
Methods: Network pharmacology was used to predict the potential targets of artemisinin. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed to identify gastric cancer-related genes. Candidate targets of artemisinin against gastric cancer were obtained by intersecting the above datasets. Three machine learning algorithms, namely random forest, LASSO regression, and SVM-RFE, were further employed to screen hub targets, followed by molecular docking to evaluate the binding affinity between artemisinin and the hub targets. Finally, anti-gastric cancer effects and mechanisms were validated by in vitro experiments (CCK-8, EdU, colony formation, Transwell and Western blot).
Results: A total of 27 candidate targets of artemisinin against gastric cancer were identified. Cross-screening by the three machine learning algorithms ultimately identified TOP2A , CCNB2 , and CCNA2 as hub targets. Molecular docking revealed low binding energies (all below −5.0 kcal/mol) between artemisinin and these three targets, suggesting potential binding affinity. In vitro experiments demonstrated that artemisinin significantly downregulated the mRNA expression levels of the three targets and suppressed the proliferation, migration, and invasion of gastric cancer cells. Mechanistic investigation showed that artemisinin treatment decreased p-ERK levels, and the anti-gastric cancer effects of artemisinin were partially reversed by the ERK pathway activator C16-PAF.
Conclusions: In vitro , artemisinin suppresses ERK/MAPK pathway activation and reduces the mRNA expression of cell cycle-related TOP2A , CCNB2 and CCNA2 , leading to inhibited proliferation, migration and invasion of gastric cancer cells.
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BibTeX
@article{hu2026exploring,
title = {{Exploring the potential mechanism of artemisinin against gastric cancer based on network pharmacology, machine learning and experimental validation}},
author = {Chenchen Hu and Yu Zhao and Minghui Yun and Yu Shen and Weimin Lian and Bin Liu and Yong Li and Changhui Deng and Bowen Hou and Jiageng He},
journal = {Frontiers in Oncology},
year = {2026},
month = oct,
doi = {10.3389/fonc.2026.1931227},
url = {https://www.frontiersin.org/articles/10.3389/fonc.2026.1931227/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Exploring the potential mechanism of artemisinin against gastric cancer based on network pharmacology, machine learning and experimental validation
AU - Chenchen Hu
AU - Yu Zhao
AU - Minghui Yun
AU - Yu Shen
AU - Weimin Lian
AU - Bin Liu
AU - Yong Li
AU - Changhui Deng
AU - Bowen Hou
AU - Jiageng He
PY - 2026
DA - 2026/10/27
JO - Frontiers in Oncology
DO - 10.3389/fonc.2026.1931227
UR - https://www.frontiersin.org/articles/10.3389/fonc.2026.1931227/full
AB - Background: Artemisinin, a natural active component extracted from Artemisia annua , exhibits potential anti-gastric cancer activity; however, its specific targets and molecular mechanisms remain unclear. Methods: Network pharmacology was used to predict the potential targets of artemisinin. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed to identify gastric cancer-related genes. Candidate targets of artemisinin against gastric cancer were obtained by intersecting the above datasets. Three machine learning algorithms, namely random forest, LASSO regression, and SVM-RFE, were further employed to screen hub targets, followed by molecular docking to evaluate the binding affinity between artemisinin and the hub targets. Finally, anti-gastric cancer effects and mechanisms were validated by in vitro experiments (CCK-8, EdU, colony formation, Transwell and Western blot). Results: A total of 27 candidate targets of artemisinin against gastric cancer were identified. Cross-screening by the three machine learning algorithms ultimately identified TOP2A , CCNB2 , and CCNA2 as hub targets. Molecular docking revealed low binding energies (all below −5.0 kcal/mol) between artemisinin and these three targets, suggesting potential binding affinity. In vitro experiments demonstrated that artemisinin significantly downregulated the mRNA expression levels of the three targets and suppressed the proliferation, migration, and invasion of gastric cancer cells. Mechanistic investigation showed that artemisinin treatment decreased p-ERK levels, and the anti-gastric cancer effects of artemisinin were partially reversed by the ERK pathway activator C16-PAF. Conclusions: In vitro , artemisinin suppresses ERK/MAPK pathway activation and reduces the mRNA expression of cell cycle-related TOP2A , CCNB2 and CCNA2 , leading to inhibited proliferation, migration and invasion of gastric cancer cells.
SN - 2234-943X
ER -