Liwei Zhou, Hanwen Lu, Wenpeng Zhao, Weijie Yu, Sifang Chen, Bingchang Zhang, Zhangyu Li, Xin Gao, Wenhua Li, Jianyao Mao, Yuanyuan Xie, Guowei Tan, Zhanxiang Wang
Department of Neurosurgery, Xiamen Key Laboratory of Brain Center, The First Affiliated Hospital of Xiamen University, Xiamen, China.
The Department of Neuroscience, Institute of Neurosurgery, School of Medicine, Xiamen University, Xiamen, China.
Cancer Biol Ther. 2025 Dec;26(1):2529643. doi: 10.1080/15384047.2025.2529643. Epub 2025 Jul 7.
Hypoxia as a hallmark of solid malignancies compromises therapeutic efficacy and prognosis. This study deciphers the functional role of hypoxia-induced ferroptosis in glioma prognosis. Hypoxia-related transcripts and ferroptosis markers were curated from public databases. ConsensusClusterPlus identify hypoxia-based molecular subtypes, while LASSO-penalized Cox regression integrated with limma-based differential expression analysis screened prognostic ferroptosis genes. Subsequent risk modeling was validated against clinical parameters and extended through nomogram construction. Protein-protein interaction networks centered on HIF-1αidentified high-confidence interactors, with parallel immune correlation analysis completing the systems-level investigation.Based on 27 hypoxia-associated genes, we stratified samples into three distinct hypoxic clusters. Differential analysis of 123 ferroptosis markers across clusters, combined with univariate Cox regression and LASSO regression, identified 23 hypoxia-induced ferroptosis genes for constructing a prognostic model. The model demonstrated robust predictive accuracy with AUC values of 0.80 (1-year), 0.86 (3-year), and 0.86 (5-year). GSEA revealed significant enrichment in ECM-receptor interactions, focal adhesion, JAK-STAT signaling, and p53 signaling pathways, suggesting their involvement in hypoxia-induced ferroptosis. Our risk model significantly outperformed conventional clinical parameters (pathology, grade, age, primary/recurrent status). Protein-protein interaction analysis incorporating HIF-1αand the 23-model genes identified XBP1 and VEGFA co-expression as significant positive prognostic factor. The immune infiltration analysis further indicated that M0 macrophages may participate in the regulation of the prognosis of VEGFA-XBP1.Hypoxia-induced ferroptosis modulation emerges as a prognostic factor in gliomas, with XBP1 and VEGFA representing druggable nodes for novel combination therapies.
缺氧作为实体恶性肿瘤的一个标志,会影响治疗效果和预后。本研究解读了缺氧诱导的铁死亡在胶质瘤预后中的功能作用。从公共数据库中筛选出与缺氧相关的转录本和铁死亡标志物。ConsensusClusterPlus软件识别基于缺氧的分子亚型,而结合基于limma的差异表达分析的LASSO惩罚Cox回归筛选出预后铁死亡基因。随后的风险模型通过临床参数进行验证,并通过列线图构建进行扩展。以HIF-1α为中心的蛋白质-蛋白质相互作用网络确定了高可信度的相互作用分子,同时进行的平行免疫相关性分析完成了系统水平的研究。基于27个与缺氧相关的基因,我们将样本分为三个不同的缺氧簇。对各簇间123个铁死亡标志物进行差异分析,并结合单变量Cox回归和LASSO回归,确定了23个缺氧诱导的铁死亡基因用于构建预后模型。该模型显示出强大的预测准确性,1年、3年和5年的AUC值分别为0.80、0.86和0.86。基因集富集分析(GSEA)显示,在细胞外基质-受体相互作用、粘着斑、JAK-STAT信号通路和p53信号通路中显著富集,表明它们参与缺氧诱导铁死亡。我们的风险模型显著优于传统临床参数(病理、分级、年龄、原发/复发状态)。包含HIF-1α和23个模型基因的蛋白质-蛋白质相互作用分析确定XBP1和VEGFA共表达为显著的阳性预后因素。免疫浸润分析进一步表明,M0巨噬细胞可能参与VEGFA-XBP1预后的调节。缺氧诱导的铁死亡调节成为胶质瘤的一个预后因素,XBP1和VEGFA代表新型联合治疗的可靶向节点。