Identification of novel prognostic targets in glioblastoma using bioinformatics analysis

Abstract

Background: Glioblastoma (GBM) is the most malignant grade of glioma. Highly aggressive characteristics of GBM and poor prognosis cause GBM-related deaths. The potential prognostic biomarkers remain to be demonstrated. This research builds up predictive gene targets of expression alterations in GBM utilizing bioinformatics analysis.

Methods and results: The microarray datasets (GSE15824 and GSE16011) associated with GBM were obtained from Gene Expression Omnibus (GEO) database to identify the differentially expressed genes (DEGs) between GBM and non-tumor tissues. In total, 719 DEGs were obtained and subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for function enrichment analysis. Furthermore, we constructed protein-protein Interaction (PPI) network among DEGs utilizing Search Tool for the Retrieval of Interacting Genes (STRING) online tool and Cytoscape software. The DEGs of degree > 10 was selected as hub genes, including 73 upregulated genes and 21 downregulated genes. Moreover, MCODE application in Cytoscape software was employed to identify three key modules involved in GBM development and prognosis. Additionally, we used the Gene expression profiling and interactive analyses (GEPIA) online tool to further confirm four genes involving in poor prognosis of GBM patients, including interferon-gamma-inducible protein 30 (IFI30), major histocompatibility complex class II-DM alpha (HLA-DMA), Prolyl 4-hydroxylase beta polypeptide (P4HB) and reticulocalbin-1 (RCN1). Furthermore, the correlation analysis indicated that the expression of IFI30, an acknowledged biomarker in glioma, was positively correlated with HLA-DMA, P4HB and RCN1. RCN1 expression was positively correlated with P4HB and HLA-DMA. Moreover, qRT-PCR and immunohistochemistry analysis further validated the upregulation of four prognostic markers in GBM tissues.

Conclusions: Analysis of multiple datasets combined with global network information and experimental verification presents a successful approach to uncover the risk hub genes and prognostic markers of GBM. Our study identified four risk- and prognostic-related gene signatures, including IFI30, HLA-DMA, P4HB and RCN1. This gene sets contribute a new perspective to improve the diagnostic, prognostic, and therapeutic outcomes of GBM.

Keywords: Bioinformatics analysis; Biomarker; Glioblastoma multiform; Prognosis.

Fig. 1

Identification of differentially expressed genes (DEGs) in GBM. A Volcano plots of GSE15824 and GSE16011 were analyzed using log₂ FC > 2 and adjusted p-value < 0.05. Upregulated DEGs were shown in red and downregulated DEGs were shown in blue. B Hierarchical clustering analysis showing DEGs between GBM and normal tissues. C Intersection of all DEGs (n = 719) among the expression profiling of GSE15824 and GSE16011

Fig. 2

Functional enrichment analysis of DEGs in GBM. A–C Gene ontology (GO) analysis of DEGs. D Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis of DEGs

Fig. 3

The protein–protein interaction (PPI) network and hub gene analysis of DEGs in GBM. A Module 1, B module 3 and C module 10 in the protein–protein interaction (PPI) network

Fig. 4

Confirmation of hub gene expression in GBM tissues. Gepia online tool was utilized to analyze the expression levels of six hub genes (FN1, PYCARD, RCN1, P4HB, HLA-DMA and IFI30) in TCGA-GBM tumors (n = 163) vs TCGA normal + GTEx normal tissues (n = 207). Data were analyzed with one-way ANOVA. Kaplan–Meier overall survival (OS) curves comparing high and low expression group of hub genes in GBM patients

Fig. 5

Identification of prognostic genes. Disease-free survival (DFS) of selected genes (RCN1, P4HB, HLA-DMA and IFI30) in GBM using TCGA database and Gepia online tool

Fig. 6

Correlation analysis between prognostic genes. A Heat map of the correlation between RCN1, P4HB, HLA-DMA and IFI30. The blue represents positive correlation. Spearman correlation analysis B between IFI30 and HLA-MDA (R = 0.79), C between RCN1 and P4HB (R = 0.43), D between IFI30 and P4HB (R = 0.3), E between IFI30 and RCN1 (R = 0.22)

Fig. 7

Principal component analysis between the GBM and LGG or non-tumor brain tissue groups based on screened prognostic-related genes. PCA 2D scatter plots and scree plots showed within-sample variation between GBM and brain-cortex (A), brain-hippocampus (B) or LGG (C) based on screened prognostic-related gene set. Each color of dot represents individual tumor

Fig. 8

The expression of P4HB, HLA-MDA and RCN1 in GBM and normal brain tissues. A–C qRT-PCR analysis of P4HB, HLA-MDA and RCN1 mRNA expression in GBM tissues (n = 15) and normal brain tissues (n = 10). Data were showed as mean + SD and analyzed with unpaired t test. **p < 0.01, ***p < 0.001. D Immunohistochemical analysis of P4HB, HLA-MDA and RCN1 expression in GBM. E IHC score in GBM tissues and normal brain tissues. Data were showed as mean + SD and analyzed with two-way ANOVA, followed by Bonferroni's multiple comparisons test. ***p < 0.001