野葛可能通过网络药理学和 AutoDock 从单细胞水平治疗前列腺癌：临床发现和药物靶点。
Pueraria lobata Potentially Treating Prostate Cancer on Single-Cell Level by Network Pharmacology and AutoDock: Clinical Findings and Drug Targets.
机构信息
Department of Emergency, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 53000, China.
出版信息
Comput Math Methods Med. 2022 Nov 21;2022:3758219. doi: 10.1155/2022/3758219. eCollection 2022.
BACKGROUND
Prostate cancer (PCa) is one of the common malignant tumors of the urological system, and metastasis often occurs in advanced stages. Chemotherapy is an effective treatment for advanced PCa but has limitations in terms of efficacy, side effects, multidrug resistance, and high treatment costs. Therefore, new treatment modalities for PCa need to be explored and improved.
METHODS
R language and GEO database were used to obtain differentially expressed genes for PCa single-cell sequencing. TCMSP, STITCH, SwissTargetPrediction, and PubChem databases were used to obtain the active ingredients and targets of (PL). Next, Cytoscape software was used to draw the interactive network diagram of "drug-active component-target pathway." Based on the STRING database, the protein-protein interaction network was constructed. Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes were applied for the genes. Molecular docking was used to visualize the drug-target interaction via AutoDock Vina and PyMOL. Finally, prognosis-related genes were found by survival analysis, and Protein Atlas was used for validation.
RESULTS
Four active components and 31 target genes were obtained through the regulatory network of PL. Functional enrichment analysis showed that PL played a pharmacological role in the treatment of PCa by regulating the metabolic processes of reactive oxygen species, response to steroid hormones, and oxidative stress as well as IL-17 signaling pathway, PCa, and estrogen signaling pathway. Single-cell data showed that , , , and genes were highly expressed, and molecular docking analysis showed that representative components had a strong affinity with receptor proteins. Survival analysis found that , , , , , and could predict progression-free survival (PFS), and some of them could be validated in PCa.
CONCLUSION
In this paper, a drug-active ingredient-target pathway network of PL at the single-cell level of PCa was constructed, and the findings revealed that it acted on genes such as , , , and to regulate several biological processes and related signaling pathways to interfere with the occurrence and development of PCa. , , , , , and were also important as target genes in predicting PFS.
背景
前列腺癌（PCa）是泌尿系统常见的恶性肿瘤之一，晚期常发生转移。化疗是治疗晚期 PCa 的有效方法，但在疗效、副作用、多药耐药性和高治疗成本方面存在局限性。因此，需要探索和改进 PCa 的新治疗方法。
方法
使用 R 语言和 GEO 数据库获取 PCa 单细胞测序的差异表达基因。使用 TCMSP、STITCH、SwissTargetPrediction 和 PubChem 数据库获取 （PL）的活性成分和靶点。然后，使用 Cytoscape 软件绘制“药物-活性成分-靶标-通路”相互作用网络图。基于 STRING 数据库构建蛋白质-蛋白质相互作用网络。使用基因本体论和京都基因与基因组百科全书对基因进行注释。通过生存分析寻找与预后相关的基因，并使用 Protein Atlas 进行验证。
结果
通过 PL 的调控网络，得到了 4 个活性成分和 31 个靶基因。功能富集分析表明，PL 通过调节活性氧物质代谢过程、对甾体激素的反应、氧化应激以及 IL-17 信号通路、PCa 和雌激素信号通路，发挥治疗 PCa 的药理作用。单细胞数据分析表明， 、 、 、 等基因表达较高，分子对接分析表明代表性成分与受体蛋白具有很强的亲和力。生存分析发现 、 、 、 、 、 等基因可以预测无进展生存期（PFS），其中一些基因可以在 PCa 中得到验证。
结论
本文构建了 PCa 单细胞水平的 PL 药物-活性成分-靶标-通路网络，发现其作用于 、 、 、 等基因，调节多个生物学过程和相关信号通路，干扰 PCa 的发生和发展。 、 、 、 、 、 等基因也是预测 PFS 的重要靶基因。
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