Gliomas have a high incidence rate in central nervous tumors. Although many breakthroughs have been made in the pathogenesis and treatment of glioma, the recurrence and metastasis rates of patients have not been improved based on the uniqueness of glioma. Glioma destroys the surrounding basement membrane (BM), leading to local infiltration, resulting in the corresponding clinical and neurological symptoms. Therefore, exploring the biological roles played by BM associated genes in glioma is particularly necessary for a comprehensive understanding of the biological processes of glioma and its treatment. Differential expression and univariate COX regression analyses were used to identify the basement membrane genes (BMGs) to be included in the model. LASSO regression was used to construct the BMG model. The Kaplan–Meier (KM) survival analysis model was used to assess the prognosis discrimination between training sets, validation sets, and clinical subgroups. Receiver-operating characteristic (ROC) analysis was used to test the prognostic efficacy of the model. Use calibration curves to verify the accuracy of nomograms. Gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and gene set enrichment analysis (GSEA) were used to analyze the function and pathway enrichment among the model groups. ESTIMATE and other 7 algorithms including CIBERSORT were used to evaluate the immune microenvironment. “pRRophetic” was used to evaluate drug sensitivity. This study demonstrated that high-risk genes (LAMB4, MMP1, MMP7) promote glioma progression and negatively correlate with patient prognosis. In the tumor microenvironment (TME), high-risk genes have increased scores of macrophages, neutrophils, immune checkpoints, chemokines, and chemokine receptors. This study suggests that BMGs, especially high-risk-related genes, are potential sites for glioma therapy, a new prospect for comprehensively understanding the molecular mechanism of glioma.