A novel nomogram predicting short-term overall survival of patients with glioma

Gliomas are the most common malignant tumors in the central nervous system. This study aimed to create a tumor survival prediction model to predict short-term overall survival in patients with glioma. In this study, the mRNAseq_325 dataset was downloaded from the Chinese Glioma Genome Atlas database as the training group, and the mRNAseq_693 dataset was downloaded as the validation group. LASSO‐COX algorithm was applied to shrink predictive factor size and build a risk score. The calibration curves and C‐Index were assessed to evaluate the nomogram's performance. This study found that the risk score, built by the LASSO‐COX algorithm, was significantly associated with overall survival in gliomas, and the nomogram, combining the risk score and clinical prognostic predictors, showed powerful prognostic ability in the training and validation groups. In conclusion, an individualized prediction model was established for predicting the short-term overall survival of glioma patients, which can provide valuable insights into identifying individuals at high risk and highlight the potential in facilitating early interventions and accurate treatment for patients with limited survival prognosis.