Integrated Metabolomics Using Data-Dependent Acquisition and Data-Independent Acquisition and Network Pharmacology to Reveal the Mechanisms of Usnic Acid in Treating Non-Small Cell Lung Cancer

文献类型： 外文期刊

第一作者： Chen, Xueyi

作者： Chen, Xueyi;Wang, Jiayi;Li, Yuxin;Zhang, Shufei;Tian, Xi;Du, Yingfeng;Guan, Shuai;Jin, Yiran;Tian, Xi

作者机构：

关键词： metabolomics; molecular mechanism; network pharmacology; NSCLC; usnic acid

期刊名称：CHEMISTRY & BIODIVERSITY （ 影响因子：2.5； 五年影响因子：2.6 ）

ISSN： 1612-1872

年卷期： 2025 年

页码：

收录情况： SCI

摘要： Usnic acid, a compound from Usneae Filum, has shown notable antitumor effects. Nevertheless, the mechanism of its anti-NSCLC action remains incompletely elucidated. This study used metabolomics, network pharmacology, molecular docking, and dynamics simulation to investigate usnic acid's potential mechanism on NSCLC utilizing A549 cell samples. The integration of metabolomics and network pharmacology was confirmed through molecular docking and molecular dynamics simulation. Combining data-dependent acquisition (DDA) and data-independent acquisition (DIA) enables maximal MS/MS coverage of endogenous substances in complex biological matrices. Metabolomics based on DDA and DIA revealed 47 potential metabolites linked to usnic acid's therapeutic effects on NSCLC. Network pharmacology identified 24 targets, with key pathways including cancer, human cytomegalovirus infection, and p53 signaling. A network analysis highlighted myeloperoxidase (MPO) as a shared target, with molecular docking and dynamics simulations confirming strong binding and stability between usnic acid and MPO. This study uncovered usnic acid's molecular mechanisms in NSCLC, primarily through MPO targeting and modulation of purine metabolism. MPO inhibition attenuates oxidative stress-driven purine catabolism, reduces uric acid-induced inflammation, and restores metabolic homeostasis. These findings illuminate novel mechanisms of usnic acid's anticancer potential and advance mechanistic insights into traditional Chinese medicine (TCM) for clinical oncology applications.

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