Interaction network of immune-associated genes affecting the prognosis of patients with glioblastoma

Abstract

Glioblastoma multiforme (GBM) is a common malignant tumor type of the nervous system. The purpose of the present study was to establish a regulatory network of immune-associated genes affecting the prognosis of patients with GBM. The GSE4290, GSE50161 and GSE2223 datasets from the Gene Expression Omnibus database were screened to identify common differentially expressed genes (co-DEGs). A functional enrichment analysis indicated that the co-DEGs were mainly enriched in cell communication, regulation of enzyme activity, immune response, nervous system, cytokine signaling in immune system and the AKT signaling pathway. The co-DEGs accumulated in immune response were then further investigated. For this, the intersection of those co-DEGs and currently known immune-regulatory genes was obtained and a differential expression analysis of these overlapping immune-associated genes was performed. A risk model was established using immune-regulatory genes that affect the prognosis of patients with GBM. The risk score was significantly associated with the prognosis of patients with GBM and had a significant independent predictive value. The risk model had high accuracy in predicting the prognosis of patients with GBM [area under the receiver operating characteristic curve (AUC)=0.764], which was higher than that of a previously reported model of prognosis-associated biomarkers (AUC=0.667). Furthermore, an interaction network was constructed by using immune-regulatory genes and transcription factors affecting the prognosis of patients with GBM and the University of California Santa Cruz database was used to perform a preliminary analysis of the transcription factors and immune genes of interest. The interaction network of immune-regulatory genes constructed in the present study enhances the current understanding of mechanisms associated with poor prognosis of patients with GBM. The risk score model established in the present study may be used to evaluate the prognosis of patients with GBM.

Keywords: Gene Expression Omnibus; The Cancer Genome Atlas; glioblastoma; immune gene; poor prognosis.

Figure 1

Screening of prognosis-related immune genes and differentially expressed transcription factors and construction of risk model. (A) Venn diagram of common DEGs among the GSE4290, GSE50161 and GSE2223 gene expression datasets. (B) Venn diagram of the intersection of DEGs and known immune-associated genes. (C) Volcano plots displaying differential expression analysis of immune-associated genes and (D) transcription factors. Red indicates high expression and blue indicates low expression. Screening conditions were set to P<0.05 and log|FC| >0.58. (E) Univariate Cox regression analysis identified 17 immune-associated genes affecting the prognosis of patients with GBM and the HR of most genes was >1. (F) Risk scoring model for immune gene construction. Red indicates death and blue denotes survival. (G) Impact of high and low risk scores on the prognosis of patients with GBM. A higher risk score indicates a worse prognosis. (H) Receiver operating characteristic curve of the risk scoring model (area under the curve=0.764). DEG, differentially expressed gene; GEO, gene expression omnibus; GBM, glioblastoma multiforme; TCGA, The Cancer Genome Atlas; FC, fold change; FDR, false discovery rate; HR, hazard ratio; ROC, receiver operating characteristic; AUC, area under the ROC curve; CXCL10, C-X-C Motif Chemokine 10; S100A10, S100 Calcium Binding Protein A10; TLR2, Toll like receptor 2; TNFAIP3, TNF Alpha Induced Protein 3; LYZ, Lysozyme; MAPK3, Mitogen-Activated Protein Kinase 3; TRIM22, Tripartite Motif Containing 22; SERPINA3, Serpin family A member 3; CXCR4, C-X-C motif chemokine receptor 4; RNASE2, Ribonuclease A Family Member 2; FCGR2B, Fc fragment of IgG receptor IIb; TNC, Tenascin C; EDNRA, Endothelin Receptor Type A; ADM, Adrenomedullin; TNFRSF12A, TNF Receptor Superfamily Member 12A; ITGB2, Integrin Subunit Beta 2; PAK1, P21 (RAC1) Activated Kinase 1.

Figure 2

Enrichment analysis of co-DEGs in the Gene Expression Omnibus datasets. Gene Ontology terms in the categories (A) CC, (B) MF and (C) BP. (D) Kyoto Encyclopedia of Genes and Genomes pathways. The results indicated that the co-DEGs were accumulated in the immune microenvironment and the AKT signaling pathway. co-DEG, common differentially expressed gene; CC, cellular component; MF, molecular function; BP, biological process; MHC, major histocompatibility complex; MAPKAP, MAP kinase-activated protein kinase.

Figure 3

Search for transcription factors related to immune genes and prognosis, and construction of interaction network. (A) Interaction network of prognosis-associated immune genes, transcription factors and immune cells. The circles indicate the immune genes, the diamonds indicate transcription factors, red nodes indicate high expression and blue nodes indicate low expression, red lines indicate positive regulation, blue lines indicate negative regulation and gray indicates no obvious correlation; red/blue dashed lines indicate possible positive/negative effects. (B-E) Survival analysis identified four transcription factors linked to immune genes affecting the prognosis of glioblastoma multiforme. (B) There were BRF1, (C) MYC, (D) SNAI2 and (E) SOX4. BRF1, BRF1 RNA polymerase III transcription initiation factor subunit; MYC, MYC proto-oncogene, BHLH transcription factor; SNAI2, Snail family transcriptional repressor 2, SOX4, SRY-Box Transcription Factor 4.

Figure 4

Analysis of clinical characteristics and transcription factor binding sites of GBM. (A) Univariate and (B) multivariate Cox regression analysis of risk scores. The results suggested that the risk score was of independent prognostic value for patients with GBM. (C) High immune and (D) high stromal scores were associated with a higher risk for patients with GBM. The immune score, stromal score and age were stratified into high and low groups using the median value as a cutoff. (E and F) Survival analysis of patients with GBM stratified by (E) the immune score and (F) stromal score. (G) Analysis of data from the Assay for Transposase-Accessible Chromatin database indicated that, in addition to the Y chromosome, there were a large number of peak sites in other chromosomes. (H) Further analysis indicated that the most binding sites were situated near the promoter (I) between 0-1 kbp, a large number of peaks were enriched near the TSS, with gradually less enrichment of peaks further away from the TSS. P<0.05 was considered to indicate statistical significance. TSS, transcription start site; GBM, glioblastoma multiforme; UTR, untranslated region; Chr, chromosome.

Figure 5

Prediction of binding sites of transcription factors and mRNA in UCSC database. (A) Analysis of the University of California Santa Cruz database revealed SNAI2 peak binding peaks in the promoter regions of the sense sequence in CXCR4 and (B) MYC peak binding peaks in the promoter regions of the sense sequence in SERPINA3, suggesting binding sites between them. CXCR4, C-X-C motif chemokine receptor 4; SERPINA3, Serpin family A member 3; SNAI2, Snail family transcriptional repressor 2.

Figure 6

Correlation analysis between risk score and immune cells. The results suggested that the risk score was positively correlated with (A) B cells, (B) CD4 T cells, (D) dendritic cells, (E) macrophages and (F) neutrophils, and (C) negatively correlated with CD8 T cells (P<0.05). Cor, correlation coefficient.