Construction and Validation of a Glioma Prognostic Model Based on Immune Microenvironment

Abstract

Objective: This study aims to construct a prognostic model based on the different immune infiltration statuses of the glioma samples.

Methods: Glioma-associated dataset was assessed from The Cancer Genome Atlas database. Hierarchical cluster analysis was performed to classify the glioma samples. Single-sample gene set enrichment analysis was introduced to the glioma samples for immune infiltration analysis. Kaplan-Meier survival analysis was applied to evaluate patients' prognoses. The differentially expressed genes (DEGs) between different sample groups were screened using limma package. Univariate Cox, LASSO Cox, and multivariate Cox regression analyses were employed to construct the prognostic model. The prediction performance of the model was examined by plotting a receiver-operating characteristic (ROC) curve, and GSEA was introduced to screen the differently activated pathways between high- and low-risk groups.

Results: The glioma samples were classified into 3 clusters where the different immune infiltration and survival statuses were presented among the clusters. 123 immune-related DEGs were screened from the differential expression analyses, and based on these DEGs, an 8-gene prognostic model was constructed. The ROC curve exhibited an optimal performance of the prognostic model, and GSEA showed that ECM-receptor interaction, complement and coagulation cascades, cytokine receptor pathways, and viral protein interaction with cytokine were differently activated between the two risk groups.

Conclusion: The current study screened an immune-associated gene set by classifying and differential analysis, followed by constructing an 8-gene prognostic model based on the screened genes.

Keywords: Feature gene; Glioma; Prediction of prognosis; Single-sample gene set enrichment analysis; Tumor microenvironment.