Identification of an Immune-Related Prognostic Risk Model in Glioblastoma - Lin, Zhiying; Wang, Rongsheng; Huang, Cuilan; He, Huiwei; Ouyang, Chenghong; Li, Hainan; Zhong, Zhiru; Guo, Jinghua; Chen, Xiaohong; Yang, Chunli; Yang, Xiaogang (Frontiers in Genetics, 2022)
Abstract Background Glioblastoma (GBM) is the most common and malignant type of brain tumour. A large number of studies have shown that the immunotherapy of tumors is effective, but the immunotherapy effect of GBM is not poor. Thus, further research of the immune-related hub genes of GBM is extremely urgent. Methods The GBM highly correlated gene clusters were screened out by differential expression, mutation analysis, and Weighted gene co-expression network analysis (WGCNA). least absolute shrinkage and selection operator (LASSO) and proportional hazards model (COX) regressions were implemented to construct prognostic risk models. Survival, the receiver operating characteristic (ROC) curve and compound difference analysis of tumor mutation burden were used to further verify the prognostic risk model. Then we predicted GBM patient responses to immunotherapy by the ESTIMATE algorithm, GSEA , and tumor immune dysfunction and Exclusion (TIDE) algorithm. Results A total of 834 immune-related differentially expressed genes (DEGs) were identificated. The five hub genes (STAT3, SEMA4F, GREM2, MDK, and SREBF1) were identified as the prognostic risk model (PRM) screened out by WGCNA and LASSO analysis of DEGs. In addition, the PRM is significant positive correlation with immune cells infiltration of tumour microenvironment (TME), and expression of critical immune checkpoints, indicating that the poor prognosis of patients is due to TIDE. Conclusions We constructed the PRM composed of five hub genes, which provided a new strategy for developing tumor immunotherapy.