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. 2021 May 19:12:610797.
doi: 10.3389/fneur.2021.610797. eCollection 2021.

Development of an Immune-Related Prognostic Index Associated With Glioblastoma

Zhengye Jiang  1   2 , Yanxi Shi  3 , Wenpeng Zhao  1   2 , Yaya Zhang  1   2 , Yuanyuan Xie  1   2 , Bingchang Zhang  1   2 , Guowei Tan  1   2 , Zhanxiang Wang  1   2
Affiliations

Development of an Immune-Related Prognostic Index Associated With Glioblastoma

Zhengye Jiang et al. Front Neurol. .

Abstract

Background: Although the tumor microenvironment (TME) is known to influence the prognosis of glioblastoma (GBM), the underlying mechanisms are not clear. This study aims to identify hub genes in the TME that affect the prognosis of GBM. Methods: The transcriptome profiles of the central nervous systems of GBM patients were downloaded from The Cancer Genome Atlas (TCGA). The ESTIMATE scoring algorithm was used to calculate immune and stromal scores. The application of these scores in histology classification was tested. Univariate Cox regression analysis was conducted to identify genes with prognostic value. Subsequently, functional enrichment analysis and protein-protein interaction (PPI) network analysis were performed to reveal the pathways and biological functions associated with the genes. Next, these prognosis genes were validated in an independent GBM cohort from the Chinese Glioma Genome Atlas (CGGA). Finally, the efficacy of current antitumor drugs targeting these genes against glioma was evaluated. Results: Gene expression profiles and clinical data of 309 GBM samples were obtained from TCGA database. Higher immune and stromal scores were found to be significantly correlated with tissue type and poor overall survival (OS) (p = 0.15 and 0.77, respectively). Functional enrichment analysis identified 860 upregulated and 162 downregulated cross genes, which were mainly linked to immune response, inflammatory response, cell membrane, and receptor activity. Survival analysis identified 228 differentially expressed genes associated with the prognosis of GBM (p ≤ 0.05). A total of 48 hub genes were identified by the Cytoscape tool, and pathway enrichment analysis of the genes was performed using Database for Annotation, Visualization and Integrated Discovery (DAVID). The 228 genes were validated in an independent GBM cohort from the CGGA. In total, 10 genes were found to be significantly associated with prognosis of GBM. Finally, 14 antitumor drugs were identified by drug-gene interaction analysis. Conclusions: Here, 10 TME-related genes and 14 corresponding antitumor agents were found to be associated with the prognosis and OS of GBM.

Keywords: CGGA; TCGA; drugs; glioblastoma; immune scores; overall survival; tumor microenvironment.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Immune scores and stromal scores are associated with glioblastoma (GBM) subtypes and their overall survival. (A) Distribution of immune scores of GBM subtypes. Violin plot shows that there is significant association between GBM subtypes and the level of immune scores (n = 309, p < 0.001). (B) Distribution of stromal scores of GBM subtypes. Violin plot shows that there is significant association between GBM subtypes and the level of stromal scores (n = 309, p < 0.001). (C) GBM cases were divided into two groups based on their immune scores: as shown in the Kaplan–Meier survival curve, median survival of the low-score group is longer than high-score group; it is not statistically different as indicated by the log rank test; p-value is 0.15. (D) GBM cases were divided into two groups based on their stromal scores: the median survival of the low-score group is longer than the high-score group; similarly, it is not statistically different as indicated by the log rank test p = 0.77.
Figure 2
Figure 2
Comparison of gene expression profile with immune scores and stromal scores in glioblastoma (GBM). (A) Volcano plot of differentially expressed genes (DEGs) of immune scores. Red, upregulated DEGs; blue, downregulated DEGs. (B) Volcano plot of DEGs of stromal scores. Red, upregulated DEGs; blue, downregulated DEGs. (C) Heatmap of the DEGs of immune scores of top half (high score) vs. bottom half (low score). p < 0.05, fold change > 1.5). (D) Heatmap of the DEGs of stromal scores of top half (high score) vs. bottom half (low score). p < 0.05, fold change > 1.5). (E,F) Venn diagrams showing the number of commonly upregulated (E) or downregulated (F) DEGs in stromal and immune score groups.
Figure 3
Figure 3
Gene ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis for differentially expressed genes (DEGs) significantly associated with immune scores. (A) Top 10 GO terms. Number of gene of GO analysis was acquired from Database for Annotation, Visualization and Integrated Discovery (DAVID) functional annotation tool. p < 0.05. (B) KEGG pathway.
Figure 4
Figure 4
Correlation of expression of individual differentially expressed genes (DEGs) in overall survival in The Cancer Genome Atlas (TCGA). Kaplan–Meier survival curves were generated for selected DEGs extracted from the comparison of groups of high (red line) and low (blue line) gene expression. p < 0.05 in log rank test. OS, overall survival in days.
Figure 5
Figure 5
Protein–protein interaction (PPI) network of differentially expressed genes (DEGs). (A) Based on the STRING online database, 207 genes/nodes were filtered into the DEG PPI network. (B) The most significant module 1 from the PPI network. (C) The second significant module 2 from the PPI network. The color of a node in the PPI network reflects the log (FC) value of the Z score of gene expression, and the size of node indicates the number of interacting proteins with the designated protein.
Figure 6
Figure 6
Gene ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis for differentially expressed genes (DEGs) significantly associated with overall survival. (A) Top 10 GO terms. Number of gene of GO analysis was acquired from Database for Annotation, Visualization, and Integrated Discovery (DAVID) functional annotation tool. p < 0.05. (B) KEGG pathway.
Figure 7
Figure 7
Validation of correlation of differentially expressed genes (DEGs) extracted from The Cancer Genome Atlas (TCGA) database with overall survival in Chinese Glioma Genome Atlas (CGGA) cohort. Kaplan–Meier survival curves were generated for selected DEGs extracted from the comparison of groups of high (red line) and low (blue line) gene expression. p < 0.05 in log rank test. OS, overall survival in days.
Figure 8
Figure 8
Data analysis workflow.
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