Prognostic Model for Glioma Patients

To construct a validated prognostic model, 609 glioma patients were randomly divided into training and testing cohorts. Ultimately, 306 patients were enrolled in the training cohort, and 303 patients were enrolled in the testing cohort. The key characteristics of each cohort are shown in Table 1. The SRlncRNA signature was derived based on the training cohort, and its potential to predict patient survival was validated utilizing the testing cohort and the whole cohort. We also confirmed the prognostic signature in the TCGA-LGG cohort, the TCGA-GBM cohort and the CGGA cohort (Supplementary Tables S1, S2).
The prognostic significance of cellular senescence-related lncRNAs was initially determined using univariate Cox regression. Least absolute shrinkage and selection operator (LASSO) regression was used to integrate the cellular senescence-related lncRNAs with p < 0.05 in univariate analysis. The LASSO results were then included in a multivariate Cox model to generate a risk score. A risk score was calculated using a linear combination of cellular senescence-related lncRNA expression levels multiplied by a regression coefficient (β): risk score = ${\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{\beta }}_{\mathrm{i}}\times$ (expression of lncRNA_i). Based on the median risk score, the patients were categorized into high-risk and low-risk groups. The log-rank test was used to compare the survival differences between the two groups.