基于网络药理学和ADMET预测的肠道菌群调控甲状腺癌的关键活性代谢物筛选及潜在作用机制探索

Screening of key active metabolites and exploration of potential mechanisms of gut microbiota in thyroid cancer through network pharmacology and ADMET prediction study

摘要

目的 通过整合网络药理学和计算分析方法，探索肠道菌群及其代谢物影响甲状腺癌(thyroid cancer，TC)的关键活性代谢物及潜在的作用机制。方法 筛选与甲状腺癌相关的核心靶点基因，并将其与关键肠道菌群及其相关代谢物/活性成分进行整合。利用Cytoscape软件构建“菌群-代谢物-核心靶点”网络，以实现调控关系的可视化。通过SwissADME服务器基于Lipinski法则评价网络中关键小分子的类药性。随后，利用admetlab 2.0平台对其进行全面的药物动力学[ADMET(Absorption)、分布(Distribution)、代谢(Metabolism)、排泄(Excretion)和毒性(Toxicity)]性质预测，包括药代动力学和安全风险评估。结果 共筛选出60个菌群代谢物与甲状腺癌的交集靶点。其中关键菌群如双歧杆菌和乳酸杆菌通过芹菜素和3-吲哚丙酸等关键代谢物，作用于甲状腺癌的枢纽基因:如肿瘤蛋白p53(tumor protein p53，TP53)、蛋白激酶B1(RAC-alpha serine/threonine-protein kinase 1，AKT1)、肿瘤坏死因子(tumor necrosis factor，TNF)。功能富集分析表明，核心靶点主要涉及磷脂酰肌醇3-激酶/蛋白激酶B(phosphatidylinositol 3-kinase/protein kinase B PI3K/AKT)、丝裂原活化蛋白激酶(mitogen-activated protein kinase MAPK)及炎症相关信号通路。分子对接显示，芹菜素与AKT1结合能为-8.59 kcal/mol，与阳性对照Capivasertib(-9.51 kcal/mol)亲和力接近。ADMET预测显示关键化合物符合Lipinski五规则，具有良好的生物利用度及较低的致癌风险。结论 本研究基于生物信息学分析，初步探索了肠道菌群影响甲状腺癌的潜在作用机制。肠道菌群可能通过芹菜素、3-吲哚丙酸等活性代谢物，多靶点、多通路调控甲状腺癌的细胞增殖与炎症微环境。这些天然化合物具有良好的成药潜力，有望成为预防和治疗甲状腺癌的潜在先导分子。

Abstract

Objective To explore the key bioactive metabolites and potential mechanisms by which the gut microbiota and its metabolites influence thyroid cancer (TC) through the integration of network pharmacology and computational analysis methods. Methods Screen for core target genes associated with thyroid cancer and integrated them with key gut microbiota and their associated metabolites/bioactive compounds. Use Cytoscape software to construct a "microbiota-metabolite-core target" network to visualize regulatory relationships. The drug-like properties of key molecules in the network were evaluated using the Lipinski rules via the SwissADME server. Subsequently, the admetSAR 2.0 platform was used to conduct a comprehensive prediction of their pharmacokinetic ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties, including pharmacokinetics and safety risk assessments. Results As a result, a total of 60 microbial metabolites were identified as common targets for thyroid cancer. Among them, key Microbial species such as Bifidobacterium and Lactobacillus act on key genes in thyroid cancer: such as, (tumor protein p53 TP53), (RAC-alpha serine/threonine-protein kinase 1 AKT1) and (tumor necrosis factor, TNF). through key metabolites including apigenin and 3-indolepropionic acid. Functional enrichment analysis indicates that the core targets primarily involve the phosphatidylinositol 3-kinase/protein kinase B (PI3K/AKT) and mitogen-activated protein kinase (MAPK), and apigenin binds to AKT1 with an energy of -8.59 kcal/mol, exhibiting affinity comparable to that of the positive control, capivasertib (-9.51 kcal/mol). ADMET prediction indicated that lead compound complies with Lipinski’s five rules, demonstrating good bioavailability and a low carcinogenic risk. Conclusion Based on bioinformatics analysis, this study provides preliminary insights into the potential mechanisms by which the gut microbiota influences thyroid cancer. The gut microbiota may regulate thyroid cancer cell proliferation and the inflammatory microenvironment through multiple targets and pathways via active metabolites such as apigenin and 3-indolepropionic acid. These natural compounds exhibit good drug-like properties and hold promise as potential lead molecules for the prevention and treatment of thyroid cancer.

关键词

甲状腺癌 / 肠道菌群 / 网络药理学 / 代谢物 / 药物动力学

Key words

Thyroid cancer / Gut microbiota / Network pharmacology / Metabolites / Pharmacokinetics

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基金资助

中山市中医药传承创新发展科研专项重点项目(2024B3004)

中山市社会公益与基础研究专项项目(2024B1135)