An integrated computational and experimental framework identifies apigenin as a potential therapeutic agent targeting EGFR/STAT3/MMP9 signaling in glioma

Background Glioma is the most common primary malignant tumor of the central nervous system and is characterized by aggressive progression, high recurrence rates, and limited therapeutic options. Apigenin, a naturally occurring flavonoid, exhibits promising anti-tumor properties; however, its molecular mechanisms in glioma remain insufficiently understood. To systematically identify the therapeutic targets and molecular mechanisms underlying apigenin-mediated anti-glioma activity through an integrated computational and experimental approach. Methods An integrated bioinformatics framework combining network pharmacology, transcriptomic analysis, and machine learning algorithms was applied to identify potential therapeutic targets of apigenin in glioma. Differentially expressed genes were screened using TCGA and GTEx datasets, and prognostic genes were identified through LASSO Cox regression, ElasticNet, and stepwise Cox regression analyses, followed by construction and validation of a multigene prognostic model. Weighted gene co-expression network analysis (WGCNA), over-representation analysis (ORA), and gene set enrichment analysis (GSEA) were performed to explore functional mechanisms. Molecular docking, molecular dynamics simulations, and ADMET prediction were conducted to evaluate the interaction stability and drug-like properties of apigenin with candidate targets. Furthermore, the predicted regulatory axis was examined in LN229 and U87 glioma cell models through cell viability assays, colony formation assays, and Western blotting. Results Six prognostic genes, including EGFR, MMP9, HIF1A, STAT3, RELA , and CASP3 , were identified and used to construct a prognostic model with robust predictive performance. The model achieved favorable discrimination, with 1-, 3-, and 5-year AUC values of 0.840, 0.876, and 0.817, respectively, and a cross-validated C-index of 0.78. Functional enrichment analyses indicated that these genes were primarily associated with cancer-related signaling pathways. Molecular docking and molecular dynamics simulations predicted potential stable interactions between apigenin and key targets, particularly EGFR and STAT3. Cellular experiments showed reduced glioma cell viability following apigenin treatment and reduced colony formation after EGFR or MMP9 knockdown, accompanied by suppression of the EGFR/STAT3 signaling pathway and downstream MMP9 expression. Conclusion This study established an integrated computational and experimental framework to investigate potential mechanisms associated with the anti-glioma activity of apigenin. Our findings suggest that apigenin-associated growth inhibition in glioma cells involves changes in EGFR/STAT3 signaling and MMP9 expression, providing new insights into its therapeutic potential and supporting further pre-clinical investigation of apigenin as a potential candidate for glioma treatment based on computational and in vitro cellular evidence.