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. 2022 Apr 27:13:832742.
doi: 10.3389/fgene.2022.832742. eCollection 2022.

Gene Expression Profiling of Glioblastoma to Recognize Potential Biomarker Candidates

Qiang Li  1 , S Aishwarya  2 , Ji-Ping Li  1 , Dong-Xiao Pan  1 , Jia-Pei Shi  3
Affiliations

Gene Expression Profiling of Glioblastoma to Recognize Potential Biomarker Candidates

Qiang Li et al. Front Genet. .

Abstract

Glioblastoma is an aggressive malignant tumor of the brain and spinal cord. Due to the blood-brain barrier, the accessibility of its treatments still remains significantly challenging. Unfortunately, the recurrence rates of glioblastoma upon surgery are very high too. Hence, understanding the molecular drivers of disease progression is valuable. In this study, we aimed to investigate the molecular drivers responsible for glioblastoma progression and identify valid biomarkers. Three microarray expression profiles GSE90604, GSE50601, and GSE134470 containing healthy and glioblastoma-affected samples revealed overlapping differentially expressed genes (DEGs). The interrelational pathway enrichment analysis elucidated the halt of cell cycle checkpoints and activation of signaling pathways and led to the identification of 6 predominant hub genes. Validation of hub genes in comparison with The Cancer Genome Atlas datasets identified the potential biomarkers of glioblastoma. The study evaluated two significantly upregulated genes, SPARC (secreted protein acidic and rich in cysteine) and VIM (vimentin) for glioblastoma. The genes CACNA1E (calcium voltage-gated channel subunit alpha1 e), SH3GL2 (SH3 domain-containing GRB2-like 2, endophilin A1), and DDN (dendrin) were identified as under-expressed genes as compared to the normal and pan-cancer tissues along with prominent putative prognostic biomarker potentials. The genes DDN and SH3GL2 were found to be upregulated in the proneural subtype, while CACNA1E in the mesenchymal subtype of glioblastoma exhibits good prognostic potential. The mutational analysis also revealed the benign, possibly, and probably damaging substitution mutations. The correlation between the DEG and survival in glioblastoma was evaluated using the Kaplan-Meier plots, and VIM had a greater life expectancy of 60.25 months. Overall, this study identified key candidate genes that might serve as predictive biomarkers for glioblastoma.

Keywords: biomarkers; gene expression; gene ontology; glioblastoma; hub genes; survival analysis.

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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
Differentially expressed genes (DEGs) of the expression profiles GSE90604, GSE50161, and GSE134470 with log2FC > 1 and p-value < 0.05. (A) Venn diagram representation of the overlapped upregulated DEGs (B) Venn diagram representation of the overlapped downregulated DEGs. (C) Volcano plots of the up- and downregulated DEGs of GSE90604. (D) Volcano plots of the up- and downregulated DEGs of GSE50161 and (E) Volcano plots of the up- and downregulated DEGs of GSE134470. Green and blue dots represent down and upregulated genes, respectively. Black dots represent the remaining genes with no significant difference.
FIGURE 2
FIGURE 2
(A) Gene Ontology (GO) term enrichment analysis of the upregulated DEGs. (B) GO analysis of the downregulated DEGs. The top ten annotations ranked based on p-values are shown for three sub-ontologies, namely, biological process, molecular function, and cellular component.
FIGURE 3
FIGURE 3
(A) Pathway enrichment analysis with KEGG and REACTOME is shown for the upregulated DEGs. (B) Interrelational pathway enrichment analysis is shown for the upregulated DEGs.
FIGURE 4
FIGURE 4
(A) Pathway enrichment analysis with KEGG and REACTOME is shown for the downregulated DEGs. (B) Interrelational pathway enrichment analysis is shown for the downregulated DEGs.
FIGURE 5
FIGURE 5
Protein–protein interaction network of the overlapped DEGs. (A) Upregulated hub genes of the PPI network with a medium confidence score of 0.4 are shown as green nodes, and hub genes with a high confidence score of 0.7 are shown as blue nodes. (B) Downregulated hub genes of the PPI with a medium score are shown as orange nodes, and hub genes with a high confidence score of 0.7 are shown as blue nodes. PPI, protein–protein interaction. DEGs, differentially expressed genes. (C) Comparison of the expression levels of hub genes among the brain tissues identified from the Expression Atlas platform for TCGA datasets.
FIGURE 6
FIGURE 6
Lollipop plot exhibiting the significant substitution mutations of genes classified either as benign or damaging. (A) Significant mutations of VIM. (B) Significant mutations of SH3GL2. (C) Significant mutations of SPARC. (D) Significant mutations of CACNA1E.
FIGURE 7
FIGURE 7
Overall survival analysis by Kaplan–Meir plots of the hub genes. (A) Overall survival rate of MAPK8IP2 in GBM patients. (B) Overall survival rate of DDN in GBM patients. (C) Overall survival rate of PPP2R2C in GBM patients. (D) Overall survival rate of VIM in GBM patients. (E) Overall survival rate of SH3GL2 in GBM patients. (F) Overall survival rate of SPARC in GBM patients. (G) Overall survival rate of BRSK1 in GBM patients. (H) Overall survival rate of CACNA1E in GBM patients.
FIGURE 8
FIGURE 8
Expression levels of the hub genes in pan-cancer tissues. (A) Lower expression levels of DDN observed in GBM. (B) Lower expression levels of SH3GL2 observed in GBM. (C) Lower expression levels of CACNA1E observed in GBM.
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