DEGs identified using RRA methods.

Glioblastoma (GBM) is the most lethal primary tumor of the central nervous system, with its resistance to treatment posing significant challenges. This study aims to develop a comprehensive prognostic model to identify biomarkers associated with temozolomide (TMZ) resistance. We employed a multifaceted approach, combining differential expression and univariate Cox regression analyses to screen for TMZ resistance-related differentially expressed genes (TMZR-RDEGs) in GBM. Using LASSO Cox analysis, we selected 12 TMZR-RDEGs to construct a risk score model, which was evaluated for performance through survival analysis, time-dependent ROC, and stratified analyses. Functional enrichment and mutation analyses were conducted to explore the underlying mechanisms of the risk score and its relationship with immune cell infiltration levels in GBM. The prognostic risk score model, based on the 12 TMZR-RDEGs, demonstrated high efficacy in predicting GBM patient outcomes and emerged as an independent predictive factor. Additionally, we focused on the molecule TSPAN13, whose role in GBM is not well understood. We assessed cell proliferation, migration, and invasion capabilities through in vitro assays (including CCK-8, Edu, wound healing, and transwell assays) and quantitatively analyzed TSPAN13 expression levels in clinical glioma samples using tissue microarray immunohistochemistry. The impact of TSPAN13 on TMZ resistance in GBM cells was validated through in vitro experiments and a mouse orthotopic xenograft model. Notably, TSPAN13 was upregulated in GBM and correlated with poorer patient prognosis. Knockdown of TSPAN13 inhibited GBM cell proliferation, migration, and invasion, and enhanced sensitivity to TMZ treatment. This study provides a valuable prognostic tool for GBM and identifies TSPAN13 as a critical target for therapeutic intervention.