Gonzalez Nazareno, Perez Küper Melanie, Garcia Fallit Matias, Nicola Candia Alejandro J, Peña Agudelo Jorge A, Suarez Velandia Maicol, Romero Ana Clara, Videla-Richardson Guillermo Agustin, Candolfi Marianela
Instituto de Investigaciones Biomédicas (INBIOMED, CONICET-UBA), Facultad de Medicina, Universidad de Buenos Aires, Paraguay 2155, 10th floor, Buenos Aires C1121ABG, Argentina.
Departamento de Fisiología, Biología Molecular y Celular, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires C1428AQK, Argentina.
Biology (Basel). 2025 May 20;14(5):572. doi: 10.3390/biology14050572.
Glioblastoma (GBM) presents significant therapeutic challenges due to its invasive nature and resistance to standard chemotherapy, i.e., temozolomide (TMZ). This study aimed to identify gene signatures that predict poor TMZ response and high PD-L1/PD-1 tumor expression, and explore potential sensitivity to alternative drugs. We analyzed The Cancer Genome Atlas (TCGA) biopsy data to identify differentially expressed genes (DEGs) linked to these characteristics. Among 33 upregulated DEGs, 5 were significantly correlated with overall survival. A risk score model was built using these 5 DEGs, classifying patients into low-, medium-, and high-risk groups. We assessed immune cell infiltration, immunosuppressive mediators, and epithelial-mesenchymal transition (EMT) markers in each group using correlation analysis, Gene Set Enrichment Analysis (GSEA), and machine learning. The model demonstrated strong predictive power, with high-risk patients exhibiting poorer survival and increased immune infiltration. GSEA revealed upregulation of immune and EMT-related pathways in high-risk patients. Our analyses suggest that high-risk patients may exhibit limited response to PD-1 inhibitors, but could show sensitivity to etoposide and paclitaxel. This risk score model provides a valuable tool for guiding therapeutic decisions and identifying alternative chemotherapy options to enable the development of personalized and cost-effective treatments for GBM patients.
胶质母细胞瘤（GBM）因其浸润性本质以及对标准化疗药物替莫唑胺（TMZ）的耐药性而面临重大治疗挑战。本研究旨在识别预测TMZ反应不佳和肿瘤PD-L1/PD-1高表达的基因特征，并探索对替代药物的潜在敏感性。我们分析了癌症基因组图谱（TCGA）活检数据，以识别与这些特征相关的差异表达基因（DEG）。在33个上调的DEG中，有5个与总生存期显著相关。利用这5个DEG构建了一个风险评分模型，将患者分为低风险、中风险和高风险组。我们使用相关性分析、基因集富集分析（GSEA）和机器学习评估了每组中的免疫细胞浸润、免疫抑制介质和上皮-间质转化（EMT）标志物。该模型显示出强大的预测能力，高风险患者生存期较差且免疫浸润增加。GSEA显示高风险患者中免疫和EMT相关通路上调。我们的分析表明，高风险患者可能对PD-1抑制剂反应有限，但可能对依托泊苷和紫杉醇敏感。这种风险评分模型为指导治疗决策和确定替代化疗方案提供了有价值的工具，从而能够为GBM患者开发个性化且具有成本效益的治疗方法。
