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Integrated 2D–3D Proteomic Profiling Identifies MLK4 as a Microenvironment‐Responsive Regulator of Chemotherapeutic Resistance in Human Glioblastoma Cells

Year: 2026
Type: article
Abstract: BACKGROUND: Therapeutic resistance is a major cause of treatment failure in glioblastoma (GBM), highlighting the need for physiologically relevant models to identify actionable resistance mechanisms. While two-dimensional (2D) cultures are widely used for target discovery, they poorly represent the tumor microenvironment. In contrast, three-dimensional (3D) spheroid cultures better recapitulate spatial heterogeneity, hypoxic gradients, and stress-adaptive signaling observed in tumors. METHODS: We applied an integrated 2D-3D quantitative proteomic approach to identify microenvironment-dependent regulators of chemoresistance in GBM. Proteomic profiling was performed in U87MG and U251MG cells grown as 2D monolayers or 3D spheroids. Differentially expressed proteins were validated by quantitative RT-PCR, and functional studies were conducted using genetic depletion followed by assessment of temozolomide (TMZ) sensitivity. RESULTS: Comparative analysis identified 13 proteins consistently differentially expressed between 2D and 3D cultures: NDUFB5, RNGTT, MLK4, SYN1, DDX5, EIF2AK2, ITGA1, ZNF33B, ZNF343, WDR19, JPH3, CCT8L2, and FNDC3A. Among these, Mixed Lineage Kinase 4 (MLK4) showed strong and reproducible upregulation in 3D spheroids in both GBM cell lines. Genetic depletion of MLK4 significantly increased TMZ sensitivity without affecting basal cell viability, suggesting a specific role in therapy response. Notably, MLK4 expression was induced only under 3D conditions. CONCLUSION: MLK4 functions as a microenvironment-responsive regulator of chemoresistance in GBM. These findings demonstrate that 3D culture systems reveal clinically relevant resistance pathways not detectable in conventional 2D models and highlight 3D proteomic profiling as a powerful strategy for identifying therapeutically actionable targets.
Language: English
Subfield: Genetics
Field: Medicine
Open Access status: hybrid
Estimated APC paid: $3,730
Award: G208497721
OpenAlex ID: W7159860271
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