Wu Bo, Zheng Congying, Mao Chengliang
Department of Neurosurgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China.
Department of Neurosurgery, Chongqing General Hospital, School of Medicine, Chongqing University, Chongqing, China.
Front Immunol. 2025 Aug 29;16:1642107. doi: 10.3389/fimmu.2025.1642107. eCollection 2025.
Glioblastoma (GBM) is the most common and aggressive primary malignant tumor of the adult central nervous system. Despite multimodal therapy, its prognosis remains poor, with a median overall survival of 14-16 months. While rare genetic syndromes and prior cranial irradiation have been implicated, definitive environmental or biological risk factors for GBM remain elusive.
In this retrospective study, we analyzed data from 94 patients with pathologically confirmed GBM and 94 matched non-tumor controls treated at Guangdong Academy of Medical Sciences between 2016 and 2023. Univariate and multivariate logistic regression analyses were conducted to identify independent risk factors, which were subsequently used to construct a predictive nomogram. Model performance was assessed using concordance index (C-index), receiver operating characteristic (ROC) curves, and calibration plots in both training and validation cohorts.
Six independent risk factors were identified: serum chloride (Cl), magnesium (Mg), high-density lipoprotein cholesterol (HDL-C), uric acid (UA), eosinophil count, and basophil count. A novel nomogram incorporating these factors demonstrated strong predictive ability, with a C-index of 0.871.
We present a validated, blood-based nomogram for GBM risk prediction with high discriminative power. This model may aid clinicians in early identification and personalized management of high-risk individuals.
胶质母细胞瘤（GBM）是成人中枢神经系统最常见且侵袭性最强的原发性恶性肿瘤。尽管采用了多模式治疗，但其预后仍然很差，中位总生存期为14 - 16个月。虽然罕见的遗传综合征和既往颅脑照射与之有关，但GBM确切的环境或生物学危险因素仍不明确。
在这项回顾性研究中，我们分析了2016年至2023年期间在广东省医学科学院接受治疗的94例经病理证实的GBM患者和94例匹配的非肿瘤对照的数据。进行单因素和多因素逻辑回归分析以确定独立危险因素，随后用于构建预测列线图。在训练和验证队列中，使用一致性指数（C指数）、受试者工作特征（ROC）曲线和校准图评估模型性能。
确定了六个独立危险因素：血清氯（Cl）、镁（Mg）、高密度脂蛋白胆固醇（HDL-C）、尿酸（UA）、嗜酸性粒细胞计数和嗜碱性粒细胞计数。纳入这些因素的新型列线图显示出强大的预测能力，C指数为0.871。
我们提出了一种经过验证的、基于血液的GBM风险预测列线图，具有较高的判别能力。该模型可能有助于临床医生早期识别高危个体并进行个性化管理。
