The potential of DEirlncRNAs: A novel approach to predict glioblastoma prognosis
The potential of DEirlncRNAs: A novel approach to predict glioblastoma prognosis
Blog Article
Background: Despite tremendous evolution in therapies, the prognosis of glioblastoma (GBM) remains grim, which calls for innovative approaches to optimize chemotherapy efficacy and predict risk.Methods: The transcriptome and clinical data of GBM were acquired from the Cancer Genome Atlas (TCGA), followed by the identification of differentially expressed immune-related long noncoding RNAs (DEirlncRNAs) with Pearson correlation and limma packet analyses.Survival-related DEirlncRNA pairs were screened with univariate Cox proportional hazard regression.Prognostic markers were obtained, and risk scores were calculated with Lasso regression and multivariate Cox risk regression HEMAVOL 2 analyses.The association of the prognostic risk model with immune cell infiltration was evaluated by comprehensively analyzing tumor-infiltrating immune cells with TIMER, XCELL, CIBERSORT, QUANTISEQ, and EPIC.
Differences in half-maximal inhibitory concentration (IC50) values between the high- and low-risk groups were assessed with the Wilcoxon signed-rank test.Results: A total of 276 DEirlncRNAs were identified, followed by the visualization of their expression patterns.Two prognosis-related DEirlncRNA pairs were screened, with high accuracy and Luminaire reliability.The constructed prognostic risk model effectively distinguished between high- and low-risk patients, and significant differences were observed in survival outcomes between the high- and low-risk groups.Furthermore, risk scores were associated with tumor-infiltrating immune cells and DEirlncRNA expression.
Additionally, the risk model had a correlation with the effectiveness of commonly used chemotherapeutic agents, providing clues into potential treatment responses.Conclusions: In our study, a novel signature was constructed with paired DEirlncRNAs (regardless of their expression), which holds significant clinical predictive value and is a potential breakthrough for personalized management of GBM.