Unraveling the mechanism of core prescription in primary liver cancer: integrative analysis through data mining, network pharmacology, and molecular simulation.

In silico pharmacology Pub Date : 2025-04-16 eCollection Date: 2025-01-01 DOI:10.1007/s40203-025-00352-2
Qingsi Zhao, Gaoyue Dong, Xinyue Zhang, Xing Gao, Hongyu Li, Zhongyuan Guo, Leilei Gong, Hong Yang
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Abstract

This study aims to identify core Traditional Chinese Medicine compound prescriptions (TCM CPs) for Primary Liver Cancer (PLC) and their underlying mechanisms. A comprehensive search was conducted using China National Knowledge Infrastructure (CNKI) and the Chinese Medical Code V5.0, identifying 151 TCM CPs. Medication frequency and association rules were analyzed with TCMICS V3.0, while active compounds were identified via TCMSP and TCMIP V2.0. Targets were predicted using Swiss Target Prediction, and disease targets from DisGeNET, OMIM, and GeneCards were cross-referenced. A protein-protein interaction (PPI) network was constructed, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using DAVID. In the process of studying active compounds, an orthogonal experiment was carried out on the extraction process of relevant herbs. The results of the orthogonal experiment and range analysis showed that for the extraction rate of the extract and the content of paeoniflorin, the decoction cycles had the most significant impact, followed by soaking time and water volume. The optimal extraction conditions were determined as soaking time of 30 min, water volume of tenfold, and 3 decoction cycles. Under these conditions, the extract yield reached 42.49%, and the paeoniflorin content was 73.60 mg/25.02 g crude herb (equivalent to 2.94 mg/g). ANOVA analysis further confirmed the significance of these factors. The results revealed 109 common targets between TCM component targets and disease targets, with key targets including STAT3, SRC, AKT1, HRAS, and PIK3CA. Molecular docking showed strong binding affinities of paeoniflorin and 3,5,6,7-tetramethoxy-2-(3,4,5-trimethoxyphenyl) chromone to PLC targets, with ADME predictions favoring paeoniflorin. Furthermore, Molecular Dynamics (MD) simulations revealed that paeoniflorin maintains stable binding to the target proteins, demonstrating promising conformational stability. The CCK-8 assay demonstrated that the core TCM CP exerted a dose-dependent inhibitory effect on HepG2 cells. After 24 h of intervention, the IC50 values of paeoniflorin and the TCM CP on HepG2 cells were 17.58 μg/mL and 120.5 μg/mL, respectively, which confirmed their anti-proliferative activity against PLC. This study identifies key active compounds and investigates their roles in modulating the Ras/Raf/MEK/ERK, AKT/NF-κB, and JAK-STAT signaling pathways, offering valuable insights into the therapeutic potential of TCM for PLC treatment.

Supplementary information: The online version contains supplementary material available at 10.1007/s40203-025-00352-2.

核心方剂治疗原发性肝癌的机制:数据挖掘、网络药理学和分子模拟的综合分析。
本研究旨在确定治疗原发性肝癌的核心中药复方及其作用机制。使用中国知网(CNKI)和中国医药代码V5.0进行全面检索,确定了151个中药cp。通过TCMICS V3.0分析用药频率和关联规律,通过TCMSP和TCMIP V2.0鉴定活性化合物。使用Swiss Target Prediction预测靶标,并交叉参考来自DisGeNET、OMIM和GeneCards的疾病靶标。构建蛋白-蛋白相互作用(PPI)网络,利用DAVID对基因本体(GO)和京都基因与基因组百科全书(KEGG)通路进行富集分析。在研究活性成分的过程中,对相关药材的提取工艺进行了正交试验。正交试验和极差分析结果表明,对芍药苷提取率和含量影响最显著的是煎煮周期,其次是浸泡时间和水体积。确定最佳提取条件为浸泡时间30 min,水体积为10倍,煎煮3次。在此条件下,提取率达42.49%,芍药苷含量为73.60 mg/25.02 g(相当于2.94 mg/g)。方差分析进一步证实了这些因素的显著性。结果发现中药成分靶点与疾病靶点共有109个共同靶点,其中关键靶点包括STAT3、SRC、AKT1、HRAS和PIK3CA。分子对接显示芍药苷和3,5,6,7-四甲基氧基-2-(3,4,5-三甲氧基苯基)色素与PLC靶标具有很强的结合亲和力,ADME预测芍药苷更倾向于PLC靶标。此外,分子动力学(MD)模拟表明,芍药苷与靶蛋白保持稳定的结合,显示出良好的构象稳定性。CCK-8实验表明,核心中药CP对HepG2细胞具有剂量依赖性的抑制作用。干预24 h后,芍药苷和中药CP对HepG2细胞的IC50值分别为17.58 μg/mL和120.5 μg/mL,证实了它们对PLC的抗增殖作用。本研究确定了关键活性化合物,并研究了它们在调节Ras/Raf/MEK/ERK、AKT/NF-κB和JAK-STAT信号通路中的作用,为中医药治疗PLC的治疗潜力提供了有价值的见解。补充信息:在线版本包含补充资料,地址为10.1007/s40203-025-00352-2。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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