ArticleScientific reports2026
Identifying the potential anti-lung cancer targets of Baicalein using a network pharmacology approach.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Natural product-based nanoengineering in gastrointestinal stromal tumors: early promise and translational challenges.Journal of gastrointestinal oncology · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
Background Lung cancer remains the deadliest malignancy globally. Although therapies, including immune checkpoint inhibitors and targeted therapy, have gradually prolonged overall survival, resistance and relapse still plague clinical management. Baicalein, a naturally derived compound exhibiting potent antitumor and anti-inflammatory properties, is widely regarded as a promising candidate for the development of anticancer therapeutic agents. Nevertheless, its mechanism of action in lung cancer remains unclear. Objective This study aims to elucidate the multi-target pharmacological mechanism by which Baicalein acts in lung cancer treatment, utilizing network pharmacology, immune infiltration analysis, molecular docking, and molecular dynamics simulation. Methods Using the TCMSP, Swiss Target Prediction, and Pharm Mapper databases, we identified the relevant targets of Baicalein. Meanwhile, lung cancer-associated targets were retrieved from the GeneCards database. The targets were obtained by taking the intersection of these two sets. A protein-protein interaction (PPI) network was then constructed using Cytoscape. Furthermore, the influence of these core targets to the tumor immune microenvironment was investigated via immune infiltration analysis. Finally, we employed molecular docking to evaluate the binding affinity between Baicalein and targets, followed by molecular dynamics simulations to confirm the stability of these interactions. Results A total of 92 potential targets of Baicalein for lung cancer were identified. Five core targets, including TP53, AKT1, MAPK3, BCL2, and EGFR, were determined based on the connectivity characteristics of the PPI network. These core targets were significantly enriched in the PI3K-AKT signaling pathway. Molecular dynamics simulations displayed that the binding free energy of the AKT1-baicalein complex was - 199.8 kJ/mol. Energy decomposition analysis suggested that complex stabilization is primarily driven by shape complementarity and hydrophobic effects, rather than conventional hydrogen bonding or salt bridges. Conclusion This study reveals that the antitumor activity of Baicalein is mediated through its multi-target action on core components of the PI3K-AKT pathway, thereby offering novel insights for exploring its therapeutic targets.
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