Screening critical genes associated with malignant glioma using bioinformatics analysis
- Authors:
- Published online on: September 12, 2017 https://doi.org/10.3892/mmr.2017.7471
- Pages: 6580-6589
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Copyright: © Xu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
As a type of tumor derived from glial cells in the spine or brain (1), gliomas account for ~80% of malignant brain tumors, and 30% of central nervous system and brain tumors (2). According to their histological features, gliomas are classified into astrocytomas, ependymomas, oligodendrogliomas, optic nerve gliomas, mixed gliomas and brainstem gliomas, among which astrocytomas are the common type of primary brain tumor among adults (3). Gliomas are also divided into low-grade gliomas (grade I and II) and high-grade gliomas (grade III and IV) according to the World Health Organization classification criteria (4). High grade gliomas are malignant gliomas comprising glioblastoma multiforme and anaplastic astrocytomas, and the median overall survival rates of patients with glioblastoma multiforme and anaplastic astrocytomas are ~15 months and 3 years, respectively (5). Therefore, elucidation of the mechanisms underlying malignant gliomas and the development of novel therapeutic strategies are urgently required.
Several studies have investigated the genes in involved in gliomas. In children with malignant gliomas, the overexpression of p53 is closely associated with adverse outcomes, independently of histological findings and clinical prognostic factors (6,7). As a critical mediator of the unfolded protein response, 78-kDa glucose-regulated protein (GRP78) is significantly upregulated in human malignant glioma and associated with its proliferation rate, suggesting that drugs capable of inhibiting GRP78 may be applied in the treatment of malignant glioma (8). Promoter methylation-induced silencing of the O6-methylguanine-DNA methyltransferase DNA-repair gene contributes to longer survival rates in patients with glioblastoma who are treated with alkylating agents (9). A previous study demonstrated that the increased level of hypoxia-inducible factor-1α (HIF-1α) is critical for the activation of glioma cell motility through affecting molecules associated with invasion (10,11). The Decoy receptor 3 (DcR3) soluble protein may be implicated in the immune evasion and progression of malignant gliomas through inhibiting CD95 ligand (CD95 L) (12). It has also been reported that the overexpression of Neuropilin 1 (NRP1) promotes tumor progression and is associated with poor prognosis in glioma (13). Spy1, which belongs to the Speedy/RINGO family, is correlated with the poor prognosis in patients with glioma and may serve as an independent prognostic predictor for patients with the disease (14). However, a comprehensive understanding of the mechanisms underlying gliomas is required.
In 2014, Guan et al used newly sequenced glioma datasets and downloaded glioma gene expression profiles to investigate the association between known molecular subtypes of grade IV glioblastoma (GBM) with grade II/III gliomas (GII/III), and found shared patterns between the GBMs and GII/IIIs (15). Using the data deposited by Guan et al (15), the present study further identified the differentially expressed genes (DEGs) in three comparison groups (grade III, vs. grade II, grade IV, vs. grade II, and grade IV, vs. grade III), and their functions were predicted using enrichment analysis. Subsequently, protein-protein interaction (PPI) networks were constructed and module analysis was performed to analyze the interactions among the DEGs. In addition, glioma-associated genes and glioma marker genes among the DEGs were screened to identify the key genes implicated in malignant glioma.
Materials and methods
Microarray data
The gene expression profile of GSE54004, which was sequenced on the platform of GPL18281 Illumina HumanHT-12 WG-DASL V4.0 R2 expression beadchip (gene symbol version), was downloaded from the Gene Expression Omnibus database (http://www.ncbi.nih.gov/geo). To identify the key genes involved in the progression of malignant glioma, a total 143 samples were selected from the GSE54004 dataset, including 12 grade II astrocytomas, 33 grade III astrocytomas and 98 grade IV astrocytomas. The astrocytomas were collected from patients with glioma, following which the tissues were fixed in formalin and embedded in paraffin at MD Anderson Cancer Center (Houston, TX, USA) (15). The GSE54004 dataset was deposited by Guan et al (15), and this study by Guan et al (15) was approved by the Institutional Review Board of MD Anderson Cancer Center, with informed consent provided by all participants.
DEG screening
The downloaded gene expression files were merged to obtain a gene expression matrix. Using the Limma package (16) in R (version 3.22.7, http://www.bioconductor.org/packages/release/bioc/html/limma.html), the DEGs in three comparison groups (grade III, vs. grade II, grade IV, vs. grade II, and grade IV, vs. grade III) were analyzed. The thresholds of P<0.05 and |log2fold change (FC)|≥1 were used. VennPlex software (version 1.0.0.2; http://www.irp.nia.nih.gov/bioinformatics/vennplex.html) enables the screening of upregulated, downregulated or contraregulated individual factors between complex data sets (17). Venn diagram analysis was performed for the DEGs screened from different groups using VennPlex software (17) (version 1.0.0.2; http://www.irp.nia.nih.gov/bioinformatics/vennplex.html).
Functional and pathway enrichment analysis
Gene Ontology (GO; http://www.geneontology.org/) can be utilized to predict the potential functions for genes and their products in terms of molecular function (MF), cellular component (CC) and biological process (BP) (18). The Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.jp/kegg/) database contains integrated knowledge of information regarding biochemical reactions and compounds, information on proteins and genes, and information on molecular interaction networks (19). GO functional and KEGG pathway enrichment analyses were performed separately for the upregulated and downregulated genes in each comparison group, using Database for Annotation, Visualization and Integrated Discovery software (DAVID version 6.7; http://www.david.niaid.nih.gov). Those terms involving at least two genes and with a P-value of P<0.05 were selected.
PPI network and module analyses
The Search Tool for the Retrieval of Interacting Genes (STRING version 10.0; http://string-db.org/) web resource and database contributes to the identification of PPIs, involving functional and physical associations (20). The PPI associations among the DEGs in each comparison group were searched using the STRING database (21), with a combined score (required confidence) >0.9. Following this, the PPI networks for the DEGs in each comparison group were visualized separately using Cytoscape software (version 3.2.0; http://www.cytoscape.org/) (22). In the PPI network, the degrees of nodes were determined by the number of edges involved, and nodes with higher degrees were determined as key nodes. Module analysis was also performed for the PPI networks using the ClusterONE tool (23).
Identification of glioma-associated genes and glioma marker genes
The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) (24) collects gene-disease, chemical-disease and chemical-gene interactions, which are manually searched from scientific literature through strict text mining using structured notation, ontologies and controlled vocabularies. Combined with the CTD database, glioma-associated genes and glioma marker genes among the DEGs screened for each group were analyzed.
Results
DEG analysis
With the thresholds of P<0.05 and |log2FC| ≥1, the DEGs in the three comparison groups were analyzed. Compared with grade II samples, a total of 27 DEGs (grade III, vs. grade II), including nine upregulated and 18 downregulated genes, were screened in the grade III samples. A total of 1,446 DEGs (grade IV, vs. grade II), including 643 upregulated and 803 downregulated genes, were identified in the grade IV samples. A total of 776 DEGs (grade IV, vs. grade III), including 410 upregulated and 366 downregulated genes, were identified in the grade IV samples relative to the grade III samples. There were more DEGs in the grade III and grade IV samples.
Venn diagram analysis showed that 20 DEGs, including five upregulated and 15 downregulated genes, were common genes between the grade III, vs. grade II and grade IV, vs. grade II comparison groups. In addition, 698 DEGs, including 356 upregulated and 342 downregulated genes, were common genes between the grade IV, vs. grade II, and grade IV, vs. grade III comparison groups (Fig. 1).
Functional and pathway enrichment analysis
No functions were enriched for the upregulated genes in the grade III, vs. grade II group. However, the downregulated genes in the grade III, vs. grade II group were significantly enriched in functions including synaptic transmission (GO_BP; P=8.06E-04) and plasma membrane (GO_CC; P=1.98E-02; Table I). No pathways were enriched for the DEGs in the grade III, vs. grade II group.
For the upregulated genes in the grade IV, vs. grade II group, functions including extracellular matrix organization (GO_BP; P=2.64E-17), extracellular matrix (GO_CC; P=2.90E-25), which involves matrix metallopeptidase 9 (MMP9) and extracellular matrix structural constituent (GO_MF; P=4.83E-10), in addition to the systemic lupus erythematosus pathway (P=4.70E-16), were significantly enriched. For the downregulated genes in the grade IV, vs. grade II group, functions including transmission of nerve impulse (GO_BP; P=1.12E-20), synapse (GO_CC; P=1.96E-21) and ion channel activity (GO_MF; P=1.35E-18), in addition to the neuroactive ligand-receptor interaction pathway (P=6.62E-07), were significantly enriched (Table II).
 | Table II.Functions and pathways separately enriched for the upregulated and downregulated genes in the grade IV, vs. grade II group. |
For the upregulated genes in the grade IV, vs. grade III group, terms including extracellular matrix organization (GO_BP; P=9.69E-20), extracellular region part (GO_CC; P=5.11E-28), extracellular matrix structural constituent (GO_MF; P=3.47E-13), which involves chitinase 3-like 1 (CHI3L1), and ECM-receptor interaction (P=1.35E-10) were significantly enriched. For the downregulated genes in the grade IV, vs. grade III group, terms including transmission of nerve impulse (GO_BP; P=1.02E-14), synapse (GO_CC; P=4.37E-22), calcium ion binding (GO_MF; P=2.79E-13) and calcium signaling pathway (P=2.15E-05) were significantly enriched (Table III).
| Table III.Functions and pathways separately enriched for the upregulated and downregulated genes in the grade IV, vs. grade III group. |
PPI network and module analyses
The PPI network constructed for the DEGs in the grade IV, vs. grade II group had 489 nodes and 1,244 interactions (Fig. 2), in which four significant modules were identified (Fig. 3). The PPI network constructed for the DEGs in the grade IV, vs. grade III group had 243 nodes and 500 interactions (Fig. 4), from which two significant modules were identified (Fig. 5). The nodes with higher degrees, including MMP9, vascular endothelial growth factor A (VEGFA) and annexin A1 (ANXA1), in the two networks are listed in Table IV. MMP9 interacted with VEGFA in these two PPI networks. No PPI network was constructed for the DEGs in grade III, vs. grade II group. The pathways enriched for the genes involved in the modules are shown in Fig. 6.
| Table IV.Top 15 nodes with highest degrees in the grade IV, vs. grade II protein-protein interaction networks. |
Identification of glioma-associated genes and glioma marker genes
The glioma-associated genes and glioma marker genes among the DEGs screened for each group were further analyzed using the CTD database. In general, 81.48, 86.93 and 89.95% of the DEGs in the grade III, vs. grade II, grade IV, vs. grade II, and grade IV, vs. grade III comparison groups, respectively, were glioma-associated genes. Of note, there were 10 common glioma marker genes, including MMP9, CHI3L1, VEGFA and S100 calcium binding protein A4 (S100A4) between the grade IV, vs. grade II, and grade IV, vs. grade III comparison groups (Table V).
| Table V.Numbers of glioma-associated genes and glioma marker genes among the differentially expressed genes screened for each group. |
Discussion
In the present study, a total of 27 (nine upregulated and 18 downregulated), 1,446 (643 upregulated and 803 downregulated) and 776 (410 upregulated and 366 downregulated) DEGs were identified in the grade III, vs. grade II, grade IV, vs. grade II, and grade IV, vs. grade III comparison groups, respectively. Venn diagram analysis showed that 20 DEGs, including five upregulated and 15 downregulated genes, were common genes between the grade III, vs. grade II and grade IV, vs. grade II comparison groups. A total of 698 DEGs, including 356 upregulated and 342 downregulated genes) were common genes between the grade IV, vs. grade II and grade IV, vs. grade III comparison groups. Four significantly modules were identified from the PPI network constructed for the DEGs in the grade IV, vs. grade II group, and two significantly modules were identified from the PPI network constructed for the DEGs in the grade IV, vs. grade III group. No PPI network was constructed for the DEGs in the grade III, vs. grade II group. It was found that 81.48, 86.93 and 89.95% of the DEGs in the grade III, vs. grade II, grade IV vs. grade II, and grade IV vs. grade III comparison groups, respectively, were glioma-associated genes. In addition, there were 10 common glioma marker genes, including MMP9, CHI3L1, VEGFA and S100A4, between the grade IV, vs. grade II and grade IV, vs. grade III comparison groups.
Inhibiting the expression of MMP9 through RNA interference represses the malignancy of glioma cells, indicating that it can be applied in the treatment of malignant gliomas (25–27). MMP2 and MMP9 have significant effects on the degradation of extracellular matrix (ECM) and angiogenesis, and on the invasiveness of gliomas, therefore, they can be utilized in targeted therapy of malignant glioma (28). CHI3L1 and MMP-9 are overexpressed in malignant gliomas, and can serve as a predictors of survival rates in patients with the disease (29). In addition, CHI3L1 is important in regulating local invasiveness and malignant transformation in gliomas, therefore, CHI3L1 may be a used as a molecular target in the treatment of gliomas (30). In patients with glioma, the serum level of CHI3L1, which encodes a secreted glycoprotein, is associated with tumor grade and possibly tumor burden in glioblastoma multiforme (31). Functional enrichment analyses have shown that MMP9 and CHI3L1 were separately enriched in the ECM and its structural constituent, respectively. ECM rigidity can mediate the invasion of glioblastoma multiforme cells through actomyosin contractility (32,33). These findings indicate that MMP9 and CHI3L1 may function in the progression of malignant gliomas through the ECM.
VEGF is an effective mediator of vascular permeability, and its inhibition can decrease tumor burden and edema production in malignant glioma (34). The growth and progression of astrocytoma is dependent on neovascularization, and the angiogenesis factor VEGFA may be essential for the infiltrative and aggressive growth of astrocytomas (35). VEGFA affects the neovascularization and invasion of glioblastoma, not only by promoting endothelial mitogenesis and permeability, but also by regulating MMP2 (36). In the two PPI networks in the present study, VEGFA interacted with MMP9, indicating that VEGFA may also affect malignant gliomas via interacting with MMP9.
The expression of S100A4 is promoted by neutrophil infiltration, and targeting S100A4 may be promising in reducing antiangiogenic therapy resistance and inhibiting the glioma malignant phenotype (37). S100A4/Mts1 has a higher expression in high-grade glioblastomas, compared with low-grade astrocytic tumors, indicating that it has an effect on brain tumor progression (38). ANXA1, targeted by forkhead box M1 (FOXM1) has a high expression in gliomas and can function as a predictor of poor prognosis in patients with the disease (39). A previous study demonstrated that ANXA1 may contribute to maintaining brain homeostasis and may be used as chemotherapeutic target in the treatment of glioblastoma multiforme (40). Therefore, S100A4 and ANXA1 may be involved in the development of malignant gliomas.
In conclusion, the present study identified 27, 1,446 and 776 DEGs in the grade III, vs. grade II, grade IV, vs. grade II, and grade IV vs. grade III comparison groups respectively. It was found that MMP9, CHI3L1, VEGFA, S100A4 and ANXA1 may act in the progression of malignant gliomas. However, these findings were obtained from bioinformatics analysis and require further validation.
Acknowledgements
This study was financially supported by the Heilongjiang Province Postdoctoral Scientific Research Developmental Fund (grant no. LBH-Q14113).
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