| 摘要: |
| 目的 通过网络药理学研究预测清胰化积方主要成分作用的靶点，探讨其治疗胰腺癌的作用机制。方法 采用中药分子机制的生物信息学分析工具（BATMAN-TCM）数据库查找清胰化积方中的主要有效单体及其靶基因。建立裸小鼠胰腺癌移植瘤模型，并分为清胰化积方治疗组与对照组，对裸小鼠肿瘤组织进行基因测序，筛选胰腺癌差异表达基因。经Venn分析筛选靶标基因并对靶标基因进行京都基因与基因组百科全书（KEGG）通路分析，构建清胰化积方功能成分治疗胰腺癌的调控机制网络图。输入STRING数据库获得蛋白质-蛋白质互作网络图，利用基因表达谱数据动态分析（GEPIA）评估关键基因与胰腺癌预后的关系。结果 共获得清胰化积方潜在的149个有效成分，预测成分作用靶标共963个。对清胰化积方治疗组与对照组裸小鼠胰腺癌肿瘤组织进行基因测序，得到6 039个差异表达基因。经Venn分析，筛选出248个共靶标靶基因。KEGG通路富集分析发现清胰化积方治疗胰腺癌的作用机制可能与MAPK、FoxO、cAMP、cGMP-PKG等信号通路有关。与对照组相比，清胰化积方治疗组裸小鼠MAP2K1（MEK1）、MAPK3（ERK）、MAP2K3（MKK3）和MAPK13（p38）表达水平下降。GEPIA结果显示，MAP2K1（MEK1）、MAPK3（ERK）、MAP2K3（MKK3）和MAPK13（p38）高表达时胰腺癌预后差。结论 清胰化积方治疗胰腺癌的作用机制与MAPK信号通路有关，MAP2K1（MEK1）、MAPK3（ERK）、MAP2K3（MKK3）和MAPK13（p38）可作为清胰化积方治疗胰腺癌的潜在预后因子。 |
| 关键词: 网络药理学 胰腺肿瘤 清胰化积方 MAPK信号通路 |
| DOI：10.16781/j.0258-879x.2020.11.1236 |
| 投稿时间：2019-09-30修订日期：2020-03-24 |
| 基金项目:国家自然科学基金（81403248），2019年度希思科-丽珠中医药肿瘤研究基金（Y-L2019-06）. |
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| Network pharmacology-based mechanism study of Qingyi Huaji recipe in treating pancreatic cancer |
| XIE Jing,CHENG Jian-shan,ZHU Xiao-yan,LIU Lu-ming,SONG Li-bin,MENG Zhi-qiang* |
(Department of Integrated Chinese and Western Medicine, Fudan University Shanghai Cancer Center, Shanghai 200032, China
*Corresponding author) |
| Abstract: |
| Objective To predict the target of the main components of Qingyi Huaji recipe (QYHJ) through network pharmacology investigations, and to explore its mechanism in treating pancreatic cancer. Methods The Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine (BATMAN-TCM) database was used to identify the major effective components of QYHJ and their target genes. The xenograft model of pancreatic cancer was established in nude mice, and they were divided into QYHJ-treated group and control group. The tumor tissues of nude mice were sequenced to screen the differentially expressed genes. The target genes were screened by Venn analysis, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to identify the related pathways; then the regulatory mechanism network of QYHJ in treating pancreatic cancer was constructed. The protein-protein interaction network was obtained from the STRING database, and Gene Expression Profiling Interactive Analysis (GEPIA) was used to evaluate the relationship between the key genes and the prognosis of pancreatic cancer. Results A total of 149 potential active components and 963 predicted targets were obtained. Gene sequencing of pancreatic cancer tissues of nude mice (QYHJtreated group vs control group) showed 6 039 differentially expressed genes. Venn analysis showed 248 potential targets and KEGG pathway enrichment analyses found that the mechanism of QYHJ in treating pancreatic cancer might involve mitogenactivated protein kinase (MAPK), forkhead box O (FoxO), cyclic adenosine monophosphate (cAMP), cyclic guanosine monophosphate-dependent protein kinase or protein kinase G (cGMP-PKG) and other signaling pathways. Compared with the control group, the QYHJ-treated group showed suppressed MAP2K1 (MEK1), MAPK3 (ERK), MAP2K3 (MKK3), and MAPK13 (p38) expressions. GEPIA results showed that the high expression levels of MAP2K1 (MEK1), MAPK3 (ERK), MAP2K3 (MKK3) and MAPK13 (p38) were related to the poor prognosis of pancreatic cancer patients. Conclusion The mechanism of QYHJ in treating pancreatic cancer may be related to MAPK signaling pathways. MAP2K1 (MEK1), MAPK3 (ERK), MAP2K3 (MKK3) and MAPK13 (p38) may be potential prognostic factors of QYHJ for treating pancreatic cancer. |
| Key words: network pharmacology pancreatic neoplasms Qingyi Huaji recipe MAPK signaling pathway |
