Abstract
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.