Construction and Validation of an Immune-Related Risk Score Model for Survival Prediction in Glioblastoma - Ren, Wei; Jin, Weifeng; Liang, Zehua (Frontiers in Neurology, 2022)
Background: As one of the most important brain tumors, GBM has a poor prognosis, especially in adults. IRGs and immune cell infiltration are responsible for the pathogenesis of GBM. This study aimed to identify new tumor markers to predict the prognosis of patients with GBM. Methods: The TCGA database and ImmPort database were used for model construction. The Wilcoxon rank-sum test was applied to identify the DEIRGs. Univariate Cox regression analysis and Kaplan–Meier analysis was performed to investigate the relationship between each DEIRG and overall survival. Next, multivariate Cox regression analysis was exploited to further explore the prognostic potential of DEIRGs and construct a risk-score model. The AUC values were calculated to assess the effect of the model prediction. Furthermore, the CGGA dataset was used for model validation. STRING database and functional enrichment analysis were used for exploring the gene interactions and the underlying functions and pathways. The CIBERSORT algorithm was used for correlation analysis of the marker genes and the tumor-infiltrating immune cells. Results: There were 198 DEIRGs in GBM, including 153 upregulated genes and 45 downregulated genes. Seven marker genes were filtered out by sequential screening for DEIRGs. The regression coefficients and expression data of the marker genes were used to construct the model. The AUC values for 1, 2, and 3 years were 0.744, 0.737, and 0.749 in the TCGA–GBM cohort and 0.612, 0.602, and 0.594 in the CGGA-GBM cohort, respectively. The results of enrichment analysis revealed that these genes were enriched in the activation of T cell and cytokine receptor interaction pathways. The interaction network map demonstrated a close relationship between the marker genes MMP9 and CCR2. Infiltration analysis of the immune cells showed that DCs could identify GBM, while LYNX1, RUNX1, and CCR2 were significantly positively correlated with DCs expression. Conclusion: This study analyzed the expression of IRGs in GBM and identified seven marker genes for the construction of an immune-related risk score model. These marker genes were found to be associated with DCs and were enriched in similar immune response pathways. These findings are likely to provide new insights for the immunotherapy of GBM.