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<p>Baiying Juhua Decoction (BYJHD) is a well-established traditional Chinese herbal formula primarily composed of Solanum lyratum and chrysanthemum, which necessitates a thorough investigation to clarify its mechanisms in combating non-small cell lung cancer (NSCLC). This study employed a combination of network pharmacology predictions, serum pharmacochemistry analysis, and various machine learning algorithms (including LASSO, SVM-RFE, and RF) to identify 38 bioactive compounds that target 653 proteins associated with NSCLC. A cross-analysis of 2161 differentially expressed genes (DEGs) and 3124 functional modules led to the identification of 54 critical therapeutic targets. Following this, protein-protein interaction (PPI) and machine learning analysis pinpointed five key signaling regulators. Molecular docking studies demonstrated strong binding affinities between four representative compounds from BYJHD and these targets. Both in vitro and in vivo experiments confirmed that BYJHD inhibits the progression of NSCLC by exerting anti-angiogenic effects, specifically through the inhibition of the ACVRL-1/Smad/ID-1 signaling pathway and the downregulation of CD34. These findings effectively connect traditional clinical applications with contemporary mechanistic insights, positioning BYJHD as a promising multi-target therapeutic candidate for NSCLC.</p> Graphical abstract <p></p>

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Anti-angiogenesis effect of Baiying Juhua Decoction on the non-small cell lung cancer: integrating pharmacology, multi-machine learning and experimental investigation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月24日

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  • Xiangwei Meng,
  • Yuan Cao,
  • Qi Shen,
  • Hongyu Zhu,
  • Jianqiao Zhang,
  • Mingxin Dong

摘要

Baiying Juhua Decoction (BYJHD) is a well-established traditional Chinese herbal formula primarily composed of Solanum lyratum and chrysanthemum, which necessitates a thorough investigation to clarify its mechanisms in combating non-small cell lung cancer (NSCLC). This study employed a combination of network pharmacology predictions, serum pharmacochemistry analysis, and various machine learning algorithms (including LASSO, SVM-RFE, and RF) to identify 38 bioactive compounds that target 653 proteins associated with NSCLC. A cross-analysis of 2161 differentially expressed genes (DEGs) and 3124 functional modules led to the identification of 54 critical therapeutic targets. Following this, protein-protein interaction (PPI) and machine learning analysis pinpointed five key signaling regulators. Molecular docking studies demonstrated strong binding affinities between four representative compounds from BYJHD and these targets. Both in vitro and in vivo experiments confirmed that BYJHD inhibits the progression of NSCLC by exerting anti-angiogenic effects, specifically through the inhibition of the ACVRL-1/Smad/ID-1 signaling pathway and the downregulation of CD34. These findings effectively connect traditional clinical applications with contemporary mechanistic insights, positioning BYJHD as a promising multi-target therapeutic candidate for NSCLC.

Graphical abstract