Objective To construct a prognostic model based on extracellular matrix （ECM）-associated genes， to evaluate its ability in predicting the prognosis of glioma patients， and to investigate the characteristics of the immune microenvironment of glioma based on this model.Methods The TCGA and GTEx databases were used to obtain the data of glioma and normal brain tissue， and then differentially expressed genes were obtained. The Gene Ontology （GO） database was used to obtain ECM-related genes， and a univariate Cox regression analysis was used to obtain the genes associated with the prognosis of glioma. The above three groups of genes were intersected to obtain overlapping candidate genes， and a LASSO analysis was used to obtain the optimal four-gene prognostic model. The glioma cohorts in the TCGA and CGGA databases were used for survival analysis and the Cox regression analysis. A prognostic nomogram was constructed based on the four-gene prognostic model and the prognostic data of patients in TCGA， which was validated in the glioma cohort in CGGA. Finally， enrichment analysis， immune checkpoint analysis， and immune infiltration analysis were used to investigate the characteristics of immune microenvironment associated with the four-gene prognostic model.Results The LASSO analysis was performed for the 22 overlapping candidate genes to obtain the optimal four-gene prognostic model， and the risk score of this model could better predict the prognosis of glioma patients in TCGA and CGGA and was a risk factor for the poor prognosis of patients. Cell line validation experiments showed that the U251 cell line （human-derived glioma cells） had higher expression levels of all four optimal genes than the HA1800 cell line （human-derived astrocytes cells）， which was consistent with the results of data analysis for TCGA and CGGA databases. The prognostic nomogram constructed based on the four-gene prognostic model also had a good ability to predict patient prognosis. Compared with the low-risk group， the high-risk group had higher levels of M2 macrophage infiltration and immune checkpoint-related molecules （PD-L1， B7-H3， CTLA4， PD1， TIM3， and LAG3） in tumor tissue.Conclusion Both the model based on ECM-associated genes and the prognostic nomogram can well predict the prognosis of glioma patients. Patients in the high-risk group have the characteristics of suppressive immune microenvironment， and immune checkpoint inhibitors may be a potential treatment modality for such patients.