Establishment And Validation Of Glioma Immune-Related Genes Prognostic Model And Exploration Of Immune Subtype Classification

Background and objectivesThe prognosis of patients with glioma,especially glioblastoma,is poor.Tumor immune microenvironment plays an important role in the formation and development of glioma.However,the immunotherapy effect of glioma is poor,so it is necessary to conduct in-depth analysis and research on immune-related genes(IRGs)of glioma,find IRGs that have an important impact on the prognosis of glioma,clarify the role of IRGs and immune microenvironment in glioma,and conduct accurate risk stratification of glioma patients based on IRGs to construct immune subtype classification.It provides a new target and molecular diagnostic index for the immunotherapy of glioma,and provides a theoretical basis for the accurate individualized treatment of glioma and the improvement of prognosis.MethodsThe glioma data from the Tumor Genome Atlas Project Database(TCGA)were selected as the training set by means of bioinformatics analysis.Differentially expressed genes and differentially expressed IRGs were screened by edgeR package,and their biological functions were analyzed by gene enrichment analysis.On this basis,univariate Cox regression analysis selected survivation-related IRGs,and multivariate Cox and Lasso linear regression analysis was used to construct glioma prognostic risk assessment model.The calculation formula according to the risk score is:h(t,X)=h0(t)exp(β1X1+β2X2+β3X3+……+βmXm),each patient was given a risk score and divided into groups according to the median risk score.The predictive performance of the model was evaluated by KM survival curve,C-index and ROC curve,and a Nomo diagram was constructed for clinical application.Then,glioma data sets from the Chinese glioma Genome Atlas Database(CGGA)were used as validation sets for external verification.GSEA enrichment method was used to compare KEGG signaling pathways in different risk groups.ESTIMATE,TIMER and other techniques were used to evaluate the relationship between risk score and tumor immune microenvironment.Drug sensitivity analysis was performed using the CellMiner database.Finally,unsupervised cluster analysis was used to identify immune subtypes closely associated with glioma prognosis,and to compare differences in gene expression and immune checkpoint among different immune subtypes.ResultsA total of 352 differentially expressed genes were identified,157 up-regulated genes and 195 down-regulated genes.There were 32 differentially expressed IRGs,with 15 up-regulated genes and 17 down-regulated genes.Univariate Cox regression analysis of differentially expressed IRGs showed that CD1D,TMSB15A,F2R,BIRC5 and other 17 IRGs were closely related to the prognosis of glioma.The 17 IRGs were analyzed by Lasso regression,and 7 IRGs(BIRC5,HNF4G,PTGER3,TNFRSF10C,TSHR,APOBEC3B,SIGLEC7)were selected by Cox analysis.The immunorelated prognostic model of glioma was constructed.According to the median risk value of 1.450266,the high and low risk groups were delimited.According to KEGG pathway analysis,cell cycle pathways,complement and coagulation cascades,cytokine-cytokine receptor interactions,ECM-receptor interactions,and adhesive plaques were significantly enriched in the high-risk group.In addition,multivariate Cox regression analysis showed that PRS,tumor grade,age,1p19q co-deletion,and risk score were independently correlated with overall survival.ROC analysis of 1-year(AUC=0.794),2-year(AUC=0.857)and 3-year(AUC=0.857)survival rates indicated that the model had good prognostic value.The graph has good accuracy in predicting 1-or 2-year,3-or 5-year survival in glioma patients.Correlation analysis between prognostic risk model and immune cell infiltration level showed that prognostic risk score was positively correlated with immune score.Activated CD4+T cells,activated CD8+T cells,activated dendritic cells,macrophages and neutrophils were higher in the high-risk group,and were negatively correlated with overall survival(OS)of glioma patients.Most checkpoint genes(e.g.,CD44,LAG3,CD274,CD244,HAVCR2,CTLA4)were up-regulated in the high-risk group and were significantly positively correlated with the risk score.Secondly,we investigated the relationship between gene and drug sensitivity in the model.Finally,four immune subtypes significantly related to the prognosis of glioma were identified and identified,which were successively C1,C2,C3 and C4 from short to long in terms of survival time.The expressions of BIRC5,HNF4G and APOBEC3B in subtypes C1,C2,C3 and C4 decreased gradually,and patients with high expressions of BIRC5,HNF4G and APOBEC3B may have worse prognosis.Three important immune checkpoints,CD276,PDCD1LG2,CD80,showed significant pairings between C1,C2,C3 and C4 subtypes,and their expression levels decreased gradually.ConclusionImmune-related gene prognostic risk model can well predict the prognosis of glioma patients,and this risk score is an independent risk factor for clinical prognosis of glioma patients.Combined with the correlation with immune microenvironment,it provides a new idea for the prognosis and immune response evaluation of glioma patients,and proposes a personalized clinical diagnosis and treatment plan for glioma patients.Finally,the classification of glioma patients was attempted to prove the possibility of classification,which provided a new research direction for the classification and diagnosis and treatment of glioma subtypes.