Potential therapeutic targets for glioblastoma patients based on a prognostic risk model

Purpose: Differentially expressed genes (DEGs) of glioblastoma (GBM) affects prognosis and the reason is unknown. In our analysis, we aimed to build a prognostic risk model, and find out some potential therapeutic targets. Methods: : The data were from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA), and , we screened for (DEGs) related to overall survival (OS). The prognostic model related to DEGs were constructed by Lasso regression analysis and we verified that the model was associated with prognosis. Then GSEA enrichment analysis was used to explore the difference of KEGG signal pathway between high and low risk groups. In addition, we also studied the immune correlation characteristics between the high and low risk groups of the model and the drug sensitivity analysis of the positive contribution of the model risk. Results: : Six DEGs (PTPRN, PTPRN2, RUFY2, CRNDE, ZNF581, SLC25A48) significantly related to the prognosis of GBM were screened, and the model of DEGs related to prognosis of GBM was successfully constructed. In addition, several signal pathways might promote tumor progression in the high risk group. Drug sensitivity analysis of chemotherapeutic drugs based on model risk genes expression. Conclusion: This model is not only an independent prognostic index of GBM, but also has higher prediction accuracy than