Tumor microenvironment (TME) cells are important elements in tumor tissue. There is increasing evidence that they have important clinical pathological significance in predicting tumor clinical outcomes and therapeutic effects. However, no systematic analysis of TME cell interactions in glioblastoma (GBM) has been reported. We systematically analyzed the transcriptional sequencing data of GBM to find an immune gene marker to predict the clinical results of GBM. First, we downloaded the expression profiles and clinical follow-up information of GBM from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). CIBERSORT was used to evaluate the infiltration mode of TME in 757 patients, systematically correlated TME phenotype with genomic characteristics and clinicopathological characteristics of GBM, defined four TME phenotypes, and TMEScore was constructed using algorithms such as random forest and principal component analysis. There is a significant correlation between TMEScore and age of onset. High TMEScore samples are characterized by immune activation, TGF pathway activation, and high expression of immune checkpoint genes, while low TMEScore samples are characterized by high-frequency IDH1 and MET mutations. Therefore, a comprehensive landscape depicting the TME characteristics of GBM may help explain GBM's response to immunotherapy and provide new strategies for cancer treatment. In this study, TMEScore can be used as a new prognostic marker to predict the survival of GBM patients, and as a potential predictor of immune checkpoint inhibitor response.