Sparganii Rhizoma (SR) has demonstrated promising anticancer effects across various malignancies; however, its mechanisms in laryngeal cancer (LC) remain poorly understood. This study employs network pharmacology and molecular docking to investigate the molecular mechanisms underlying SR’s therapeutic effects on LC, providing novel insights for its potential use in treatment. Active compounds and targets of SR were identified through the TCMSP and Pharmmapper databases, while LC-related targets were sourced from GEO, GeneCards, OMIM, and PharmGkb databases. A Venn diagram generated from these datasets highlighted 58 overlapping targets. The STRING database and Cytoscape 3.9.1 software facilitated the construction of a protein–protein interaction network for these targets, and R language analysis revealed 15 core targets. GO and KEGG enrichment analyses, conducted with the ‘‘clusterProfiler’’ package, identified relevant biological processes, cellular components, and molecular functions associated with LC treatment. KEGG analysis suggested SR primarily regulates pathways such as TNF, IL-17, and P53. Molecular docking confirmed SR’s ability to bind effectively to the 15 core targets. Molecular dynamics simulations further validated stable protein–ligand interactions for MAPK1, GSK3B, and MAPK14. Core target validation across transcriptional, translational, and immune infiltration levels was performed using GEPIA, HPA, cBioPortal, and TIMER databases. In conclusion, network pharmacology, molecular docking, and dynamics simulations provided insights into SR’s mechanism in LC treatment, forming a theoretical basis for further investigation of its therapeutic potential.

Sparganii Rhizoma （SR） 已在各种恶性肿瘤中显示出有希望的抗癌作用;然而，其在喉癌 （LC） 中的机制仍然知之甚少。本研究采用网络药理学和分子对接来研究 SR 对 LC 治疗作用的分子机制，为其在治疗中的潜在应用提供新的见解。SR 的活性化合物和靶标通过 TCMSP 和 Pharmmapper 数据库鉴定，而液相色谱相关靶标来自 GEO、GeneCards、OMIM 和 PharmGkb 数据库。从这些数据集生成的维恩图突出显示了 58 个重叠的目标。STRING 数据库和 Cytoscape 3.9.1 软件促进了这些靶标的蛋白质-蛋白质相互作用网络的构建，R 语言分析揭示了 15 个核心靶标。使用“clusterProfiler”软件包进行 GO 和 KEGG 富集分析，确定了与 LC 处理相关的生物过程、细胞成分和分子功能。KEGG 分析表明 SR 主要调节 TNF 、 IL-17 和 P53 等通路。分子对接证实了 SR 与 15 个核心靶点有效结合的能力。分子动力学模拟进一步验证了 MAPK1、GSK3B 和 MAPK14 的稳定蛋白质-配体相互作用。使用 GEPIA 、 HPA 、 cBioPortal 和 TIMER 数据库进行跨转录、翻译和免疫浸润水平的核心靶标验证。综上所述，网络药理学、分子对接和动力学模拟为 SR 在 LC 治疗中的机制提供了见解，为进一步研究其治疗潜力奠定了理论基础。