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.胶质母细胞瘤（Glioblastoma, GBM）是中枢神经系统中致死性最强的原发性肿瘤，其对治疗的耐药性带来了显著的临床挑战。本研究旨在构建一套全面的预后模型，以筛选与替莫唑胺（temozolomide, TMZ）耐药相关的生物标志物。我们采用多维度研究策略，联合差异表达分析与单因素Cox回归分析，在GBM样本中筛选出替莫唑胺耐药相关差异表达基因（TMZ resistance-related differentially expressed genes, TMZR-RDEGs）。随后通过LASSO-Cox回归分析，筛选出12个TMZR-RDEGs以构建风险评分模型，并通过生存分析、时间依赖性ROC曲线及分层分析对模型的预测效能进行验证。为探究该风险评分的潜在作用机制及其与GBM免疫细胞浸润水平的关联，我们开展了功能富集分析与突变分析。基于这12个TMZR-RDEGs构建的预后风险评分模型，在预测GBM患者预后结局方面展现出优异的效能，且可作为独立的预后预测因子。此外，本研究聚焦于功能尚未明确的TSPAN13分子，通过体外实验（包括CCK-8实验、Edu实验、划痕愈合实验及Transwell实验）评估了细胞增殖、迁移与侵袭能力，并利用组织芯片免疫组化技术对临床胶质瘤样本中的TSPAN13表达水平进行定量分析。通过体外实验与小鼠原位移植瘤模型，我们验证了TSPAN13对GBM细胞替莫唑胺耐药性的影响。值得注意的是，TSPAN13在GBM中呈高表达状态，且与患者不良预后显著相关。敲低TSPAN13可抑制GBM细胞的增殖、迁移与侵袭能力，并增强其对替莫唑胺治疗的敏感性。本研究为GBM患者提供了一款极具价值的预后工具，并确认TSPAN13可作为GBM治疗干预的关键靶点。