Department of Colorectal and Anal Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China (mainland).
Med Sci Monit. 2019 Oct 14;25:7709-7714. doi: 10.12659/MSM.919935.
BACKGROUND The network pharmacological approach was used to identity the anti-colorectal cancer (CRC) targets of formononetin (FN) and the molecular mechanisms of FN against CRC. MATERIAL AND METHODS A tool of the DisGeNET database was used for collection of CRC-based targets. Other tools of SuperPred, herbal ingredients target (HIT), and SwissTargetPrediction databases were applied in prediction of pharmacological targets of FN against cancer. A protein-protein interaction (PPI) network of FN against CRC was obtained by using a STRING database. All top biological functional processes and signaling pathways of FN against CRC were identified by using Database for Annotation, Visualization and Integrated Discovery (DAVID) software and Omicshare cloud platform. RESULTS The most key anti-CRC targets of FN were identified as tumor protein p53 (TP53), cytochrome P450 3A4 (CYP3A4), ATP binding cassette subfamily G member 2 (ABCG2), tumor necrosis factor (TNF), epidermal growth factor receptor (EGFR), Erb-B2 receptor tyrosine kinase 2 (ERBB2), and cytochrome P450 1A1 (CYP1A1). In further assays, the treatment of CRC by FN was mainly involved in biological functional processes of reactive oxygen species metabolic process, positive regulation of transcription, DNA-templated, positive regulation of nucleic acid-templated transcription, and positive regulation of RNA metabolic process. anti-CRC by FN of signaling pathways were associated with amyotrophic lateral sclerosis (ALS), allograft rejection, cytokine-cytokine receptor interaction, asthma, mitogen-activated protein kinase (MAPK) signaling pathways, and others. CONCLUSIONS The anti-CRC molecular mechanisms of FN are implicated in suppression of cellular proliferation and regulation of cancer-related metabolic pathways. Interestingly, 8 optimal biological targets may be used as potential molecular markers for predicting and treating CRC.
采用网络药理学方法鉴定芒柄花素(FN)抗结直肠癌(CRC)的靶点及 FN 抗 CRC 的分子机制。
使用 DisGeNET 数据库中的工具收集 CRC 相关靶点;应用 SuperPred、草药成分靶点(HIT)和 SwissTargetPrediction 数据库预测 FN 抗癌的药理学靶点;利用 STRING 数据库获得 FN 抗 CRC 的蛋白质-蛋白质相互作用(PPI)网络;使用 DAVID 软件和 Omicshare 云平台鉴定 FN 抗 CRC 的生物学功能过程和信号通路。
FN 抗 CRC 的关键靶标主要有肿瘤蛋白 p53(TP53)、细胞色素 P450 3A4(CYP3A4)、三磷酸腺苷结合盒亚家族 G 成员 2(ABCG2)、肿瘤坏死因子(TNF)、表皮生长因子受体(EGFR)、Erb-B2 受体酪氨酸激酶 2(ERBB2)和细胞色素 P450 1A1(CYP1A1)。进一步研究表明,FN 治疗 CRC 主要涉及活性氧物质代谢过程、转录的正调控、DNA 模板、核酸模板转录的正调控、RNA 代谢过程的正调控等生物学功能过程;FN 抗 CRC 的信号通路与肌萎缩侧索硬化症(ALS)、同种异体移植排斥、细胞因子-细胞因子受体相互作用、哮喘、丝裂原激活蛋白激酶(MAPK)信号通路等相关。
FN 抗 CRC 的分子机制涉及抑制细胞增殖和调节与癌症相关的代谢途径。有趣的是,8 个最优的生物学靶标可作为预测和治疗 CRC 的潜在分子标志物。