An Immune-Related lncRNA Signature to Predict Survival In Glioma Patients

Glioma is the most common and fatal primary brain tumor in human. Long non-coding RNA (lncRNA), which are characterized by regulation of gene expression and chromatin recombination play an important role in glioma, and immunotherapy is a promising cancer treatment. Therefore, it is necessary to identify Immune-related lncRNAs in glioma. In this study,we collected and evaluated the RNA-seq data of The Cancer Genome Atlas (TCGA, ) and Chinese Glioma Genome Atlas (CGGA, ) glioma patients and immune-related lncRNAs were screened. Cox regression and LASSO analysis were performed to construct a risk score formula to explor the different overall survival between high- and low-risk groups in TCGA and verified with CGGA. Gene ontology (GO) and pathway-enrichment analysis (KEGG) were performed to identify the function of screened genes. Co-expression network were performed of these genes for further analysis. Eleven immune-related lncRNAs were concerned to be involved in survival and adopted to construct the risk score formula. Patients with high-risk score held poor survival both in TCGA and CGGA. Compared with current clinical data, the Area Under Curve (AUC) of different years and Principal components analysis (PCA) suggested that the formula had better predictive power. Functional Annotation of immune-related lncRNAs showed that the differences overall survival of high and low RS group might be caused by the cell differentiation, microtubule polymerization, etc. We successfully constructed an immune-related lncRNAs formula with powerful predictive function, which provides certain guidance value to the analysis of glioma pathogenesis and clinical treatment, and potential therapeutic targets for glioma treatment.

Automated summary

In this study, immune-related lncRNAs in glioma were identified by analyzing RNA-seq data from TCGA and CGGA. A risk score formula was constructed to predict overall survival, with high-risk patients showing poorer survival in both datasets. The formula demonstrated better predictive power compared to current clinical data, providing potential therapeutic targets and guidance for glioma pathogenesis and treatment.