Identification and verification on prognostic index of glioblastoma immune-related lncRNAs.

Abstract

Background: Glioblastoma (GBM) is the most common cause of primary brain malignancy. Recently, many immune-related long noncoding ribonucleic acids (ir-lncRNAs) are indicated to be closely related to the regulation of the immune microenvironment and immune cell infiltration of GBM.

Objectives: Through the joint analysis of multiple public databases, key ir-lncRNAs in GBM were screened. The ir-lncRNAs were used to construct risk-scoring models and promote the development of novel GBM biomarkers.

Material and methods: In this study, we performed a three-way Venn analysis combined with a least absolute shrinkage and selection operator (LASSO) regression analysis on all lncRNAs in The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA) and Imm-Lnc datasets, and identified 10 ir-lncRNAs. Multivariate Cox analysis was used to calculate the coefficient and construct a risk-scoring model.

Results: By plotting calibration curves and receiver operating characteristic (ROC) curves, the model showed excellent prediction results. Based on the Tumor Immune Estimation Resource (TIMER) database, the correlation analysis showed that 10 ir-lncRNAs risk scores were related to immune cell infiltration. The enrichment analysis was subsequently performed, which showed that these ir-lncRNAs played an important role in the progression of GBM. Among the 10 lncRNAs, we found that AL354993.1 was highly expressed in GBM, had not been reported, and was shown to be closely related to GBM progression.

Conclusions: In conclusion, the 10 ir-lncRNAs have the potential to predict the prognosis of GBM patients and may play a vital role in the progression of the disease.