Integrated computational analyses reveal multi-target kinase modulation as a potential mechanism underlying teriflunomide action in glioblastoma: network evidence for estrogen-associated pathway crosstalk

Glioblastoma (GBM) is the most aggressive primary malignant tumor of the central nervous system, characterized by rapid progression, therapeutic resistance, and poor prognosis. Drug repurposing offers a promising strategy for identifying novel therapeutic options for GBM. Teriflunomide, a dihydroorotate dehydrogenase inhibitor approved for multiple sclerosis, has shown potential anticancer activity; however, its molecular mechanisms in GBM remain unclear. In this study, an integrated network pharmacology approach was employed to identify potential targets and signaling pathways of teriflunomide in GBM. Protein–protein interaction analysis and functional enrichment were performed to identify key regulatory networks. Molecular docking and molecular dynamics (MD) simulations were used to evaluate the binding stability between teriflunomide and core targets. Additionally, immunohistochemical (IHC) data from the Human Protein Atlas (HPA) were used to assess the expression patterns of selected hub genes in glioma and normal brain tissues as supportive validation. As this study employed a subtype-agnostic, systems-level approach without stratification by GBM molecular subtypes, findings should be interpreted with consideration of disease heterogeneity and may not uniformly apply across classical, proneural, and mesenchymal GBM subgroups. Network analysis identified AKT1, HSP90AA1, MAP2K1, and SRC as the most dynamically stable core targets of teriflunomide in GBM. Functional enrichment identified the estrogen signaling pathway as the most significantly enriched KEGG pathway; however, given the absence of detectable ESR1 protein expression in GBM tissues by HPA immunohistochemistry, this enrichment is interpreted as reflecting kinase-mediated network crosstalk rather than canonical estrogen receptor-driven signaling. Molecular docking and MD simulations confirmed stable binding of teriflunomide to AKT1, HSP90AA1, MAP2K1, and SRC, whereas interactions with EGFR and MMP9 showed conformational instability under dynamic conditions. HPA-based IHC analysis revealed elevated protein expression of AKT1, MAP2K1, and SRC in GBM tissues compared with normal glial cells, supporting their biological relevance as primary therapeutic targets. This study suggests that teriflunomide may exert anti-GBM effects primarily through multi-target modulation of kinase-driven signaling networks centered on AKT1, MAP2K1, SRC, and HSP90AA1, with potential involvement of estrogen-associated pathway crosstalk at the network level. These findings provide a computational rationale for repurposing teriflunomide in GBM and offer a theoretical foundation for future experimental validation.