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 risks 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 thephosphatidylinositol 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.

Key words: Thyroid cancer, Gut microbiota, Network pharmacology, Metabolites, Pharmacokinetics