Zhao Yujia, Fu Zhenkai, Chen Sijie, Li Fei, Zhang Xiaoyu, Setiwalidi Kaidiriye, Ruan Zhiping, Yao Yu, Luo Lanxin
Department of Oncology, Tangdu Hospital, the Fourth Military Medical University, Xi'an, Shaanxi, China.
Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Front Oncol. 2025 Jul 21;15:1517557. doi: 10.3389/fonc.2025.1517557. eCollection 2025.
Tumor transformation and progression are accompanied by multiple carcinogenic pathways that dysregulate polyamine demand and metabolism. The importance of polyamines has demonstrated that their metabolism is a potential therapeutic strategy. Yet, few prognostic models based on polyamine metabolism-related gene risk have been developed for gliomas.
The mRNA expression profiles and variations in 37 polyamine metabolism-related genes (PMRGs) were obtained from the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. PMRGs-related risk model was constructed by least absolute shrinkage and selection operator (LASSO) Cox regression and tested for predictive ability across two independent datasets from the Gene Expression Omnibus (GEO). The landscape of the tumor immune microenvironment and drug sensitivity were investigated systematically using multiple methods based on PMRG-related risk subtypes. Weighted gene co-expression network analysis (WGCNA) was applied to identify the key prognostic genes of the PMRGs. In addition, key genes were validated with regard to their expression and prognostic significance in human glioma tissues. To verify the cell types, single-cell RNA sequencing was performed on the cohorts available at GEO.
Based on PMRG clusters, patients with glioma showed significant differences in PMRG expression, prognosis, and biological functions. A 11-gene risk model was constructed, and patients were categorized into high- and low-risk subtype according to the risk score. The high-risk subtype exhibited a poorer prognosis due to its immunosuppressive microenvironment. Furthermore, there were striking differences between the distinct subtypes in terms of immune cell infiltration, anticancer immunity cycle, tumor mutation burden, immune checkpoints, and response to targeted inhibitors. Spermine synthase (SMS) was identified as a key PMRG in patients with gliomas. A significant increase in SMS mRNA and protein expression was observed in tumors compared to normal controls. Single-cell sequencing analyses showed that SMS mRNA was highly expressed in all cell types, except oligodendrocytes.
A PMRG-related risk model can be used as a reliable prognostic biomarker in glioma treatment. In addition, polyamine metabolism and function can be successfully targeted therapeutically.
肿瘤转化和进展伴随着多种致癌途径，这些途径会失调多胺需求和代谢。多胺的重要性表明其代谢是一种潜在的治疗策略。然而，针对胶质瘤，基于多胺代谢相关基因风险的预后模型却鲜有开发。
从癌症基因组图谱（TCGA）和中国胶质瘤基因组图谱（CGGA）数据库中获取37个多胺代谢相关基因（PMRGs）的mRNA表达谱和变异情况。通过最小绝对收缩和选择算子（LASSO）Cox回归构建PMRGs相关风险模型，并在来自基因表达综合数据库（GEO）的两个独立数据集中测试其预测能力。基于PMRG相关风险亚型，使用多种方法系统地研究肿瘤免疫微环境和药物敏感性景观。应用加权基因共表达网络分析（WGCNA）来识别PMRGs的关键预后基因。此外，对关键基因在人胶质瘤组织中的表达和预后意义进行了验证。为了验证细胞类型，对GEO中可用的队列进行了单细胞RNA测序。
基于PMRG聚类，胶质瘤患者在PMRG表达、预后和生物学功能方面存在显著差异。构建了一个11基因风险模型，并根据风险评分将患者分为高风险和低风险亚型。高风险亚型由于其免疫抑制微环境而预后较差。此外，不同亚型在免疫细胞浸润、抗癌免疫周期、肿瘤突变负担、免疫检查点和对靶向抑制剂的反应方面存在显著差异。精胺合酶（SMS）被确定为胶质瘤患者的关键PMRG。与正常对照相比，肿瘤中观察到SMS mRNA和蛋白表达显著增加。单细胞测序分析表明，除少突胶质细胞外，SMS mRNA在所有细胞类型中均高表达。
PMRG相关风险模型可作为胶质瘤治疗中可靠的预后生物标志物。此外，多胺代谢和功能可以成功地作为治疗靶点。
