Zhao Qingsi, Dong Gaoyue, Zhang Xinyue, Gao Xing, Li Hongyu, Guo Zhongyuan, Gong Leilei, Yang Hong
Yanjing Medical College, Capital Medical University, Beijing, 101300 China.
College of Medicine, Henan University of Chinese Medicine, Zhengzhou, 450046 Henan China.
In Silico Pharmacol. 2025 Apr 16;13(2):63. doi: 10.1007/s40203-025-00352-2. eCollection 2025.
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 IC 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.
The online version contains supplementary material available at 10.1007/s40203-025-00352-2.
本研究旨在识别原发性肝癌（PLC）的核心中药复方及其潜在机制。利用中国知网（CNKI）和《中国医学典》V5.0进行全面检索，共识别出151个中药复方。使用中医传承辅助平台V3.0分析用药频次和关联规则，通过中药系统药理学数据库与分析平台（TCMSP）和中药整合药理学平台V2.0识别活性成分。利用瑞士靶点预测工具预测靶点，并与来自DisGeNET、OMIM和GeneCards的疾病靶点进行交叉参考。构建蛋白质-蛋白质相互作用（PPI）网络，随后使用DAVID进行基因本体（GO）和京都基因与基因组百科全书（KEGG）通路富集分析。在研究活性成分过程中，对相关药材的提取工艺进行了正交试验。正交试验及极差分析结果表明，对于提取物的提取率和芍药苷含量，煎煮次数影响最为显著，其次是浸泡时间和加水量。确定最佳提取条件为浸泡时间30分钟、加水量10倍、煎煮3次。在此条件下，提取物得率达到42.49%，芍药苷含量为73.60mg/25.02g生药（相当于2.94mg/g）。方差分析进一步证实了这些因素的显著性。结果显示中药成分靶点与疾病靶点之间有109个共同靶点，关键靶点包括信号转导与转录激活因子3（STAT3）、肉瘤病毒癌基因同源物（SRC）、蛋白激酶B1（AKT1）、哈维鼠肉瘤病毒癌基因同源物（HRAS）和磷脂酰肌醇-4,5-二磷酸3-激酶催化亚基α（PIK3CA）。分子对接显示芍药苷和3,5,6,7-四甲氧基-2-(3,4,5-三甲氧基苯基)色酮与PLC靶点具有较强的结合亲和力，药物代谢动力学（ADME）预测显示芍药苷更具优势。此外，分子动力学（MD）模拟表明芍药苷与靶蛋白保持稳定结合，显示出良好的构象稳定性。细胞计数试剂盒-8（CCK-8）试验表明核心中药复方对肝癌细胞系HepG2具有剂量依赖性抑制作用。干预24小时后，芍药苷和中药复方对HepG2细胞的半数抑制浓度（IC）值分别为17.58μg/mL和120.5μg/mL，证实了它们对PLC的抗增殖活性。本研究确定了关键活性成分，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，为中药治疗PLC的潜在疗效提供了有价值的见解。
在线版本包含可在10.1007/s40203-025-00352-2获取的补充材料。
