Identification and validation of icaritin-associated prognostic genes in hepatocellular carcinoma through network pharmacology, bioinformatics analysis, and cellular experiments

Background Hepatocellular carcinoma (HCC) is a key global health issue, marked by poor clinical outcomes and lower survival rates. Icaritin (ICT), a bioactive compound derived from traditional Chinese medicine, has shown promising multi-target antitumor properties and potential clinical benefits in the treatment of HCC; however, its precise mechanisms of action remain insufficiently understood. Therefore, this study adopted an integrative strategy that combined bioinformatics analysis, experimental validation, and network pharmacology to systematically explore the prognostic and therapeutic relevance of ICT-associated genes. Methods Initially, potential targets of ICT and HCC-associated genes were identified through extensive database screening, and the overlapping candidates were further determined using WGCNA and differential expression analysis. These core intersecting genes were subsequently refined via four complementary machine learning algorithms, KM survival analysis and LASSO Cox regression to establish a prognostic risk score model with predictive value. Additionally, molecular docking and dynamics simulations were performed to evaluate the binding stability between ICT and these targets. Finally, in vitro experiments were conducted to evaluate the effects of ICT on the proliferation and migration, as well as the expression of core target genes. Results We identified thirty-five overlapping targets between ICT and HCC, and functional enrichment analysis showed that these genes are primarily implicated in cell cycle regulation and glycolytic pathways, highlighting potential mechanisms through which ICT exerts its antitumor effects. By integrating multiple machine learning approaches, KM survival analysis and LASSO Cox regression, we developed a four-gene prognostic model that successfully stratified HCC patients into higher- and lower-risk groups. Molecular docking and molecular dynamics simulations demonstrated that ICT binds stably to core targets, supporting its potential role in modulating disease progression. In vitro validation confirmed that ICT suppresses HepG2 and Huh7 cells proliferation and migration in a dose-dependent manner, while molecular analyses demonstrated that ICT treatment significantly downregulates CA9, UCK2, and FABP5 expression and simultaneously upregulates CYP2C9, thereby supporting its role in modulating critical oncogenic pathways. Conclusion Modulation of ICT-targeted genes was found to effectively suppress HCC progression, underscoring their potential value as prognostic biomarkers and ideal therapeutic targets for the treatment of HCC.