Feng Jing, Zhao Lin, Chen Huiyan, Lin Jianhai, Shang Mingchao, Xu Baoqing, Wang Xinpeng, Ma Danyu, Zhou Jinping, Zhao Hu
Department of Radiation Oncology, 900TH Hospital of Joint Logistics Support Force, Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, China.
Department of Radiation Oncology, School of Medicine, Dongfang Hospital of Xiamen University, 900TH Hospital of Joint Logistics Support Force, Xiamen University, Fuzhou, China.
J Cell Mol Med. 2025 Feb;29(4):e70418. doi: 10.1111/jcmm.70418.
Gliomas, the most prevalent primary malignancy of the central nervous system, is characterised by its high mortality rates and unfavourable prognosis. Despite extensive research, the underlying mechanisms of glioma pathogenesis remain elusive. The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases provided the lower-grade gliomas (LGG) transcriptome and related clinical data, which were downloaded separately. It was determined what the clinical data differences were between the two groups based on the median reticulon-4 (RTN4) expression group. The R language's survminer tool was utilised to examine the variations in survival between the RTN4 high and low-expression groups. The GeneMANIA database was searched for genes that might interact with RTN4, and these genes were then used to create extensive coexpression networks. Cox regression analysis, both univariate and multivariate, was used to filter out the independent prognostic factors influencing tumour growth. Based on independent prognostic parameters, a nomogram was created to predict prognosis. The model was assessed both internally and externally using receiver operating characteristic curve (ROC) and correcting curves. The R cibersort package was utilised to assess the level of immune infiltration abundance. We further validated our findings with clinical tissues using immunohistochemistry approaches. Statistical significance was determined using the Wilcoxon signed-rank test, with a p value of < 0.05 considered significant. RTN4 expression in the tumour group was higher than in the normal group (p < 0.001), and a high-expression level was linked to a poor prognosis (p = 0.028). Patients with elevated RTN4 expression exhibited significant differences from normal brain tissue samples when stratified analysis of LGG patients by sex or radiation treatment was performed (p < 0.001). The immune cell infiltration data demonstrated that the two groups' expressions of various immune cells differed, with pDC cells showing the greatest correlation (-0.421). Univariate and multivariate Cox regression study showed that RTN4, isocitrate dehydrogenase (IDH) mutation, 1p19q codeletion and nia age could be employed as independent prognostic factors for LGG, and the correction curve of the model fit well. Ultimately, clinical samples' immunohistochemistry revealed that RTN4 was markedly overexpressed in low-grade gliomas. High RTN4 expression was strongly associated with a poor prognosis in LGG patients. RTN4 may serve as a prognostic biomarker for patients with LGG and represents a potential therapeutic target for immunotherapy in this patient population.
胶质瘤是中枢神经系统最常见的原发性恶性肿瘤,其特征是死亡率高且预后不良。尽管进行了广泛研究,但胶质瘤发病机制的潜在机制仍不清楚。癌症基因组图谱(TCGA)和中国胶质瘤基因组图谱(CGGA)数据库分别提供了低级别胶质瘤(LGG)的转录组和相关临床数据。根据网织蛋白-4(RTN4)表达中位数分组确定两组之间的临床数据差异。利用R语言的survminer工具检查RTN4高表达组和低表达组之间的生存差异。在GeneMANIA数据库中搜索可能与RTN4相互作用的基因,然后利用这些基因构建广泛的共表达网络。采用单因素和多因素Cox回归分析筛选影响肿瘤生长的独立预后因素。基于独立预后参数创建列线图以预测预后。使用受试者工作特征曲线(ROC)和校正曲线对模型进行内部和外部评估。利用R语言的cibersort包评估免疫浸润丰度水平。我们使用免疫组织化学方法在临床组织中进一步验证了我们的发现。使用Wilcoxon符号秩检验确定统计学意义,p值<0.05被认为具有显著性。肿瘤组中RTN4的表达高于正常组(p<0.001),高表达水平与预后不良相关(p=0.028)。对LGG患者按性别或放疗进行分层分析时,RTN4表达升高的患者与正常脑组织样本存在显著差异(p<0.001)。免疫细胞浸润数据表明,两组中各种免疫细胞的表达不同,浆细胞样树突状细胞(pDC细胞)的相关性最大(-0.421)。单因素和多因素Cox回归研究表明,RTN4、异柠檬酸脱氢酶(IDH)突变、1p19q共缺失和年龄可作为LGG的独立预后因素,且模型的校正曲线拟合良好。最终,临床样本的免疫组织化学显示RTN4在低级别胶质瘤中明显过表达。RTN4高表达与LGG患者的不良预后密切相关。RTN4可能作为LGG患者的预后生物标志物,并代表该患者群体免疫治疗的潜在靶点。