Tan Bo, Chen Tao, Song Peng, Lin Feng, He Shuangyin, Zhang Shiyuan, Yin Xiaohong
Department of Neurosurgery, Guangyuan Central Hospital, No. 16 Jingxiangzi, Lizhou District, Guangyuan, Sichuan Province, China.
BMC Pharmacol Toxicol. 2025 Apr 22;26(1):89. doi: 10.1186/s40360-025-00919-x.
Ethylnitrosourea (ENU) is a potent mutagen that induces gliomas in experimental models. Understanding the molecular mechanisms underlying ENU-induced gliomagenesis can provide insights into glioma pathogenesis and potential therapeutic targets.
We analyzed gene expression data from GSE16011 and GSE4290 datasets to identify differentially expressed genes (DEGs) associated with gliomagenesis. Comparative Toxicogenomics Database (CTD) was used to identify potential ENU targets. Protein-protein interaction (PPI) network, enrichment analysis, and Cox regression analysis were employed to elucidate key genes and pathways. A risk model was constructed using the TCGA dataset by LASSO analysis, and nomogram and immuno-infiltration analyses were performed.
We identified 71 common genes potentially in ENU-induced gliomas. Key hub genes, including TP53, MCL1, CCND1, and PTEN, were highlighted in the PPI network. Enrichment analysis revealed significant GO terms and KEGG pathways, such as "Neuroactive ligand-receptor interaction" and "Glioma." A risk model based on 11 prognostic genes was constructed, effectively stratifying patients into low and high-risk groups, with significant differences in overall survival. The model demonstrated high predictive accuracy. The nomogram constructed from ENU-related risk scores showed good calibration and clinical utility. Immuno-infiltration analysis indicated higher immune cell infiltration in high-risk patients. Molecular docking suggested strong binding affinities of ENU with MGMT and CA12.
Our integrative analysis identified key genes and pathways implicated in ENU-induced gliomagenesis. The ENU-related risk model and nomogram provide significant prognostic value, offering potential tools for clinical assessment and targeted therapies in glioma patients.
乙基亚硝基脲（ENU）是一种强效诱变剂，可在实验模型中诱发胶质瘤。了解ENU诱导胶质瘤发生的分子机制有助于深入了解胶质瘤的发病机制和潜在治疗靶点。
我们分析了来自GSE16011和GSE4290数据集的基因表达数据，以识别与胶质瘤发生相关的差异表达基因（DEG）。利用比较毒理基因组学数据库（CTD）确定潜在的ENU靶点。采用蛋白质-蛋白质相互作用（PPI）网络、富集分析和Cox回归分析来阐明关键基因和通路。通过LASSO分析使用TCGA数据集构建风险模型，并进行列线图和免疫浸润分析。
我们鉴定出71个可能与ENU诱导的胶质瘤相关的共同基因。PPI网络突出显示了关键的枢纽基因，包括TP53、MCL1、CCND1和PTEN。富集分析揭示了显著的基因本体（GO）术语和京都基因与基因组百科全书（KEGG）通路，如“神经活性配体-受体相互作用”和“胶质瘤”。构建了基于11个预后基因的风险模型，有效地将患者分为低风险和高风险组，总生存期存在显著差异。该模型显示出较高的预测准确性。由ENU相关风险评分构建的列线图显示出良好的校准和临床实用性。免疫浸润分析表明高风险患者的免疫细胞浸润更高。分子对接表明ENU与O6-甲基鸟嘌呤-DNA甲基转移酶（MGMT）和碳酸酐酶12（CA12）具有很强的结合亲和力。
我们的综合分析确定了与ENU诱导的胶质瘤发生相关的关键基因和通路。ENU相关风险模型和列线图具有显著的预后价值，为胶质瘤患者的临床评估和靶向治疗提供了潜在工具。
