Tang Hongyu, Zhang Ruijie, Xu Ying, Wang Xu, Gu Jingyan, Gu Lianping, Liu Yaohua
Department of Neurosurgery, Shanghai General Hospital, Shanghai, 200080, China.
Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
Discov Oncol. 2025 Aug 25;16(1):1618. doi: 10.1007/s12672-025-03459-2.
The prognosis of low-grade glioma (LGG) exhibits significant heterogeneity, and the optimal management strategy remains controversial. Therefore, identifying biomarkers associated with glioma prognosis is necessary.
Hub genes were identified and a prognostic risk signature was constructed in the TCGA cohort using LASSO Cox regression. The predictive significance of this model was assessed and confirmed in the independent CGGA cohort. Single-cell transcriptional profiling characterized the hub gene expression patterns across cell types.
We screened four hub genes (SERPINE1, KIF2C, SLC16A1, FABP5). Patients were stratified into high- and low-risk groups using this model. The high-risk group had significantly lower survival rates in both TCGA and CGGA cohorts. The model demonstrated strong predictive power (1/2/3-year AUCs: 0.85/0.85/0.83 in TCGA; 0.73/0.71 for 2/3 years in CGGA). Functionally, the high-risk group exhibited activation of pro-tumorigenic pathways, showed higher levels of immune infiltration across multiple lymphocyte and myeloid subsets, and demonstrated higher sensitivity to drugs such as AZD5582 and AZD8055, evidenced by significantly lower IC50 values. At the single-cell level, FABP5 was highly expressed in myeloid cells, and FABP5 + tumor-associated macrophages (TAMs) were enriched in lipid metabolism and antigen presentation pathways. Pseudotime analysis suggested increasing FABP5 expression during TAMs differentiation.
The constructed 4-gene hypoxia-lactylation prognostic model can effectively predict survival risk in LGG patients. The high-risk group is characterized by activation of pro-tumorigenic pathways, high immune infiltration, and sensitivity to specific targeted drugs. FABP5 + TAMs participate in LGG progression by regulating lipid metabolism and inflammatory responses, representing a potential therapeutic target.
低级别胶质瘤（LGG）的预后存在显著异质性，最佳治疗策略仍存在争议。因此，识别与胶质瘤预后相关的生物标志物很有必要。
在TCGA队列中使用LASSO Cox回归识别枢纽基因并构建预后风险特征。在独立的CGGA队列中评估并确认该模型的预测意义。单细胞转录谱分析了枢纽基因在不同细胞类型中的表达模式。
我们筛选出四个枢纽基因（SERPINE1、KIF2C、SLC16A1、FABP5）。使用该模型将患者分为高风险和低风险组。在TCGA和CGGA队列中，高风险组的生存率均显著较低。该模型显示出强大的预测能力（TCGA队列中1/2/3年AUC分别为0.85/0.85/0.83；CGGA队列中2/3年AUC分别为0.73/0.71）。在功能上，高风险组表现出促肿瘤途径的激活，在多个淋巴细胞和髓系亚群中显示出更高水平的免疫浸润，并且对AZD5582和AZD8055等药物表现出更高的敏感性，IC50值显著降低证明了这一点。在单细胞水平上，FABP5在髓系细胞中高表达，FABP5 +肿瘤相关巨噬细胞（TAM）在脂质代谢和抗原呈递途径中富集。伪时间分析表明在TAM分化过程中FABP5表达增加。
构建的4基因低氧乳酸化预后模型可以有效预测LGG患者的生存风险。高风险组的特征是促肿瘤途径激活、高免疫浸润以及对特定靶向药物敏感。FABP5 + TAM通过调节脂质代谢和炎症反应参与LGG进展，代表了一个潜在的治疗靶点。
