English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Background  As one of the most important brain tumors, glioblastoma (GBM) has a poor prognosis, especially in adults. Immune-related genes (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 Cancer Genome Atlas (TCGA) database and ImmPort database were used for model construction. The Wilcoxon rank-sum test was applied to identify the differentially expressed IRGs (DEIRGs) between the GBM and normal samples. 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. A risk-score model was constructed based on the above results. The area under the curve (AUC) values were calculated to assess the effect of the model prediction. Furthermore, the Chinese Glioma Genome Atlas (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 (LYNX1, PRELID1P4, MMP9, TCF12, RGS14, RUNX1, and CCR2) were filtered out by sequential screening for DEIRGs. The regression coefficients (0.0410, 1.335, 0.005, −0.021, 0.123, 0.142, and −0.329) 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, which indicated a high predictive power. 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 dendritic cells (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 patients with GBM.

Construction and Validation of an Immune-Related Risk Score Model for Survival Prediction in Glioblastoma