Yu Shu-Jie, Kong Xiao-Bin, Jin Xin, Shan Meng-Yi, Cheng Gang, Wang Pei-Lu, Li Wen-Long, Zhao Pei-Yuan, Sheng Yun-Jie, He Bing-Qian, Shi Qi, Li Hua-Qiang, Zhao Qi-Ming, Qin Lu-Ping, Meng Xiong-Yu
School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
Front Plant Sci. 2025 Jul 3;16:1615076. doi: 10.3389/fpls.2025.1615076. eCollection 2025.
is a traditional Chinese medicine used to treat neoplasms in previous publications. Nevertheless, how the chemical constituents treat neoplasm still needs to be clarified. Herein, we combined the response surface method, UPLC-ESI-MS/MS, network pharmacology, molecular docking, and molecular dynamics (MD) simulation to characterize bioactive constituents in and further uncover their potential mechanisms against neoplasm. As a result, the material-liquid ratio was significantly reduced from 100 g/mL to 32 g/mL, and the extraction efficiency was 1.332%, which was close to the predicted value of 1.346% in the response surface method, indicating that the algorithm model had a good fit. Next, a total of 738 compounds, including 161 terpenoids, 144 phenolic acids, 51 alkaloids, 24 flavonoids, 34 saccharides, 32 lignans and coumarins, 45 amino acids and derivatives, 23 organic acids, 134 lipids, 22 nucleotides and derivatives, and 59 other ingredients, were characterized from based on the accurate precursor and product ions, retention time, standards, fragmentation patterns, and previous publications. Subsequently, to screen which constituents were most effective, the network pharmacology was constructed, and 96 active compounds and 488 key neoplasm-related targets were identified, leading to the establishment of the "Drug-Compound-Target" network and PPI network. The top 4 components and targets were selected for the presentation of MD simulation, consisting of cytosporone C, cystomexicone A, mediterraneone, and bestim, with the highest degree related targets carbonic anhydrase 12 (CA12), carbonic anhydrase 2 (CA2), carbonic anhydrase 9 (CA9), and carbonic anhydrase 1 (CA1) being considered as the core compounds and targets. GO pathway analysis was closely related to hormone, protein phosphorylation, and protein kinase activity. KEGG pathway enrichment primarily involved pathways in cancer and the cAMP signaling pathway in cancer. Overall, this integration method provided guiding significance for the exploration of TCM treatment.
在以往的文献中,是一种用于治疗肿瘤的中药。然而,其化学成分如何治疗肿瘤仍有待阐明。在此,我们结合响应面法、超高效液相色谱-电喷雾串联质谱、网络药理学、分子对接和分子动力学(MD)模拟来表征中的生物活性成分,并进一步揭示其抗肿瘤的潜在机制。结果,料液比从100 g/mL显著降低至32 g/mL,提取效率为1.332%,接近响应面法预测值1.346%,表明该算法模型拟合良好。接下来,基于精确的前体离子和产物离子、保留时间、标准品、碎片模式及以往文献,从中总共鉴定出738种化合物,包括161种萜类化合物、144种酚酸、51种生物碱、24种黄酮类化合物、34种糖类、32种木脂素和香豆素、45种氨基酸及其衍生物、23种有机酸、134种脂质、22种核苷酸及其衍生物以及59种其他成分。随后,为筛选出最有效的成分,构建了网络药理学,鉴定出96种活性化合物和488个与肿瘤相关的关键靶点,从而建立了“药物-化合物-靶点”网络和蛋白质-蛋白质相互作用(PPI)网络。选择前4种成分和靶点进行MD模拟展示,包括环孢菌素C、囊孢米酮A、地中海酮和贝司他明,与之关联度最高的靶点碳酸酐酶12(CA12)、碳酸酐酶2(CA2)、碳酸酐酶9(CA9)和碳酸酐酶1(CA1)被视为核心化合物和靶点。基因本体(GO)通路分析与激素、蛋白质磷酸化和蛋白激酶活性密切相关。京都基因与基因组百科全书(KEGG)通路富集主要涉及癌症相关通路和癌症中的环磷酸腺苷(cAMP)信号通路。总体而言,这种整合方法为探索中医治疗提供了指导意义。