Glioblastoma multiforme (GBM) is characterized by its aggressive nature and is the most malignant form of glioma, associated with a poor clinical prognosis. This study aimed to evaluate and predict the survival outcomes of GBM patients by developing a prognostic long non-coding RNA (lncRNA) signaling model for GBM. We utilized R software to analyze The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) datasets, identifying differentially expressed genes (DEGs) using univariate Cox regression. A least absolute shrinkage and selection operator (LASSO) regression model was developed, and its predictive performance was assessed via receiver operating characteristic (ROC) curves and Kaplan-Meier survival analysis. Risk groups were validated through enrichment analysis, immune profiling, and drug sensitivity evaluation. We constructed a risk prediction model incorporating 19 lncRNAs to categorize patients into high- and low-risk groups, with significant survival differences. The model exhibited high predictive accuracy [area under the curve (AUC) =0.95]. Immune infiltration analysis revealed elevated naïve B cells, M2 macrophages, and γδ T cells in high-risk patients, alongside upregulated programmed cell death 1 (PDCD1) and T Cell Immunoglobulin and ITIM Domain (TIGIT) but downregulated CD274 [programmed death-ligand 1 (PD-L1)]. Five immune-related hub genes (DLEU1, ENSG00000259704, ENSG00000249109, LINC01574, SOX21-AS1) were identified, with SOX21-AS1 positively correlating with CD274 and favorable prognosis. Functional enrichment highlighted lipid metabolism, JAK/STAT signaling, and T-cell regulation. Drug sensitivity analysis revealed differential half-maximal inhibitory concentration (IC50) values for 20 anticancer agents between risk groups. Our findings suggest that SOX21-AS1 may influence the tumor microenvironment through the modulation of the immune checkpoint CD274, potentially serving as a novel prognostic indicator and a target for immunotherapeutic strategies in GBM.
山东省济南市章丘区文博路2号
齐鲁师范学院 genelibs生信实验室
山东省济南市高新区舜华路750号
大学科技园北区F座4单元2楼
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