Glioblastoma (GBM) is a strikingly heterogeneous and lethal brain tumor with very poor prognosis. LncRNAs play critical roles in the tumorigenesis of GBM through regulation of various cancer-related genes and signaling pathways. Here, we focused on the essential role of EMT and identified 78 upregulated EMT-related genes in GBM through differential expression analysis and Gene set enrichment analysis (GSEA). A total of 301 EMT-related lncRNAs were confirmed in GBM through Spearman correlation analysis and a prognostic signature consisting of seven EMT-related lncRNAs (AC012615.1, H19, LINC00609, LINC00634, POM121L9P, SNHG11, and USP32P3) was established by univariate and multivariate Cox regression analyses. Significantly, Kaplan–Meier analysis and receiver-operating-characteristic (ROC) curve validated the accuracy and efficiency of the signature to be satisfactory. Quantitative real-time (qRT)-PCR assay demonstrated the expression alterations of the seven lncRNAs between normal glial and glioma cell lines. Functional enrichment analysis revealed multiple EMT and metastasis-related pathways were associated with the EMT-related lncRNA prognostic signature. In addition, we observed the degree of immune cell infiltration and immune responses were significantly increased in high-risk subgroup compared with low-risk subgroup. In conclusion, we established an effective and robust EMT-related lncRNA signature which was expected to predict the prognosis and immunotherapy response for GBM patients.

Purpose: Glioblastoma (GBM) is a class of strikingly heterogeneous and lethal brain tumor with very poor prognosis. LncRNAs play critical roles in the tumorigenesis and progression of GBM through regulation of various cancer-related genes and signaling pathways. Here, we aimed to establish an epithelial-mesenchymal transition (EMT)-related lncRNA signature for GBM and explore its underlying mechanisms. Methods: Differential expression analysis and Gene set enrichment analysis (GSEA) were performed to explore key genes and signaling pathways associated with GBM. Spearman correlation analysis, Univariate and multivariate Cox regression analyses were used to construct a lncRNA prognostic signature for GBM patients. Kaplan-Meier analysis and receiver-operating-characteristic (ROC) analysis were applied to assess the performance of the prognostic signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) enrichment analyses were performed to explore the underlying mechanisms of the signature. Single-sample GSEA (ssGSEA) was employed to explore the relationship of the signature and immune activities in GBM.Results: We focused on the essential role of EMT in GBM and identified 78 upregulated EMT-related genes in GBM. A total of 301 EMT-related lncRNAs were confirmed in GBM and a prognostic signature consisting of seven EMT-related lncRNAs (AC012615.1, H19, LINC00609, LINC00634, POM121L9P, SNHG11, and USP32P3) was established, which could divide GBM patients into low- and high-risk subgroups. The accuracy and efficiency of the signature were validated to be satisfactory. Functional enrichment analysis revealed multiple EMT and metastasis-related pathways were associated with the EMT-related lncRNA prognostic signature. In addition, we found the degree of immune cell infiltration and immune responses were significantly increased in high-risk subgroup compared with low-risk subgroup. Conclusion: we established an effective and robust EMT-related lncRNA signature which is expected to predict the prognosis and immunotherapy response for GBM patients.