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. 2021 Jan 22;100(3):e23513. doi: 10.1097/MD.0000000000023513

Prediction and analysis of hub genes between glioblastoma and low-grade glioma using bioinformatics analysis

Baowei Xu 1,∗
Editor: Milind Chalishazar1
PMCID: PMC7837950  PMID: 33545929

Abstract

Gliomas are an intractable tumor in the central nervous system. The present study aimed to identify the differentially expressed genes (DEGs) between glioblastoma multiforme (GBM) and low-grade gliomas (LGG) in order to investigate the mechanisms of different grades of gliomas. The Cancer Genome Atlas (TCGA) database was used to identify DEGs between GBM and LGG, and 2641 genes have been found differentially expressed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to determine the related functions and pathways of DEGs. Protein–protein interaction (PPI) network extracted a total of 444 nodes and 1953 interactions, and identified the top 6 hub genes in gliomas. The microarray data of the datasets GSE52009 and GSE4412, which were obtained from Gene Expression Omnibus (GEO) database, were used to externally validate DEGs expression levels. Gene Expression Profiling Interactive Analysis (GEPIA) database which was based on TCGA was used to explore the survival of hub genes in LGG and GBM. Additionally, the Oncomine database and Chinese Glioma Genome Atlas (CGGA) database were used to validate the mRNA expression level and prognostic value of hub genes. Gene Set Enrichment Analysis (GSEA) identified further hub genes-related pathways. In summary, through biological information and survival analysis, 6 hub genes may be new biomarkers for diagnosis and for guiding the choice of treatment strategies for different grades of gliomas.

Keywords: bioinformatics analysis, differentially expressed genes, glioblastoma, survival analysis

1. Introduction

Gliomas are the most common primary tumor of the central nervous system.[1] According to the histopathological characteristics, gliomas can be divided into 4 grades from low to high malignant degree.[2] Low-grade gliomas (LGG) (astrocytomas, oligodendrogliomas, and oligoastrocytomas) are well-differentiated and have a 5-year survival rate of nearly 60%.[3] Despite many treatment strategies, glioblastoma multiforme (GBM) is one of the most lethal brain tumors with a median overall survival (OS) of 14.6 months.[4] Unfortunately, most LGG will progress to GBM within 5 to 10 years. In this process, the expression of many genes and molecular pathways change.[5] So far, O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation, EGFR alterations and isocitrate dehydrogenase 1 (IDH1) and isocitrate dehydrogenase 2 (IDH2) mutations have been identified as markers in molecular classification of glioblastomas.[6,7] Therefore, it is significant to explore new genes and molecular markers for the treatment of different grades of gliomas.

Gene expression profiling analyses have greatly promoted clinical oncology research, mainly in the following aspects: searching for tumor related genes, exploring molecular diagnosis, comparing therapeutic effects and predicting prognosis and recurrence of tumors.[8–10] In recent years, the application of microarray and high-throughput sequencing technology have enlarged gene expression profiling analysis to an unprecedented scale. At the same time, multiple databases and methods are used to study and verify the differentially expressed genes (DEGs), which makes bioinformatics analysis more accurate and reliable.[11,12] In addition, through the comparative analysis of DEGs, especially the study of signaling pathways and interaction networks, we can explore the occurrence, development and transformation mechanisms of malignant tumors.[13]

In this study, TCGA database was used to identify the DEGs between GBM and LGG.

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to determine the related functions and pathways of DEGs. Protein–protein interaction (PPI) network extracted important nodes and interactions in gliomas, and identified top 6 hub genes. Subsequently, 2 clinical microarray data (GSE52009 and GSE4412) were obtained from Gene Expression Omnibus (GEO) database. The clinical value of the hub genes was further explored and verified by Gene Expression Profiling Interactive Analysis (GEPIA) and Gene Set Enrichment Analysis (GSEA) based on TCGA database.[14–16] In order to further improve the reliability of this study, Oncomine database and Chinese Glioma Genome Atlas (CGGA) database were introduced for external validation of the mRNA expression level and prognostic value of hub genes.[17,18]

2. Material and methods

2.1. Datasets and identification of DEGs

The mRNA expression profiles and clinical data of LGG and GBM patients were obtained from the TCGA data portal (https://portal.gdc.cancer.gov/repository), which grades II and III of gliomas were included in the TCGA-LGG project, whereas grade IV was separated in TCGA-GBM project. Moreover, the datasets from the National Center of Biotechnology Information (NCBI) GEO database (http://www.ncbi.nlm.nih.gov/geo/) was divided into LGG and GBM according to the TCGA standard. The gene expression level and clinical data from TCGA were used for identification of DEGs, and Gene expression profile of GSE52009 and GSE4412 from GEO was used to externally validate the hub gene expression level. The GSE52009, which was based on GPL6480 Agilent-014850 Whole Human Genome Microarray was uploaded by Jiang et al, contained 92 LGG samples and 24 GBM samples. The GSE4412, which was based on GPL96 Affymetrix Human Genome U133A Array was uploaded by Nelson SF, included 26 LGG samples and 59 GBM samples.[19] To clarify the DEGs between LGG and GBM, the fold change (FC) >2 (|log2FC| ≥1) and false discovery rate (FDR) < 0.05 were used as the threshold to identify DEGs. Heat map and volcano plot were generated using the R software package pheatmap and ggplot2, respectively.

2.2. Functional and pathway enrichment analysis

GO analysis was used to determine the functions including biological pathways (BP), cellular component (CC), and molecular function (MF).[20] KEGG pathway enrichment analysis was used to systematically describe relevant pathways for the genes.[21] The clusterProfiler package was used to perform GO analysis and KEGG pathway enrichment analysis. False discovery rate (FDR) of <0.05 was used as the cut-off value.

2.3. PPI network analysis

The online Search Tool the Retrieval of Interacting Genes (STRING) database (http://www.string-db.org) was used to demonstrate the interaction between various proteins,[22] and Cytoscape software (Version 3.7.2, http://www.cytoscape.org/) was used to reconstruct a PPI network. The mapped network was detected the possible relationship among DEGs. Due to the large number of DEGs while FC >2, we identified PPI network using FC>4. The cut-off criterion of interaction confidence score was set as >0.4. The important nodes and hub genes in the network were predicted and explored by CytoHubba, a plugin of Cytoscape, and the top 10 genes were selected from the results of each method. The final top 6 hub genes in different methods were selected for further analysis.

2.4. Oncomine database analysis

The expression level of hub genes was identified in the Oncomine database (https://www.oncomine.org), and analyzed the mRNA expression differences between LGG and GBM.[17] The threshold was determined according to the following values: P value of .05, fold change of 2.0.

2.5. GEPIA database analysis

GEPIA database was used to perform survival analysis which is based on TCGA database (http://gepia.cancer-pku.cn/).[15] Survival analyses were carried out to achieve Kaplan–Meier plots. The log-rank test was performed during Kaplan–Meier survival analysis. The two-tailed P value was used in this study, and a P value of less than .05 considered statistically significant.

2.6. CGGA database analysis

Chinese Glioma Genome Atlas (CGGA) data (http://www.cgga.org.cn/), a Chinese glioma database which included over 2000 samples from the Chinese cohort, was used for external validation the mRNA expression level and prognostic value of hub genes.[23] The results were downloaded from the CGGA website.

2.7. GSEA analysis

GSEA was conducted to further identify hub genes-related pathways. GSEA version 4.0.3 software (http://www.broadinstitute.org/gsea) was used to analyze data. The expression level of hub genes was used as the phenotype annotation, and patients in TCGA cohort were divided into low and high categories. The Molecular Signatures Database (MSigDB) of c2 (c2.cp.kegg.v7.1.symbols.gmt) was used to assess the functional differences between the low and high hub gene expression groups.[24] The number of permutations was set to 1000 and the phenotype labels were high and low expression. FDR <0.25 and P < .05 were set as the cut-off criteria to confirm significant gene sets.

3. Results

3.1. Identification of DEGs between LGG and GBM

Gene expression profiles from the TCGA database were conducted to analyze DEGs. A total of 529 LGG and 169 GBM samples was involved in our further analysis. Differentially expressed genes between GBM and LGG were identified as explained in the methods. The results revealed that a total of 2641 genes were identified to be differentially expressed, including 1428 up-regulated genes and 1213 down-regulated genes (Fig. 1A,B).

Figure 1.

Figure 1

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Identification of expression differences between LGG and GBM. (A) Heat-map overview of the differentially expressed genes. (B) Volcano plot of the differential mRNA expression analysis.

3.2. Functional and pathway enrichment analysis of DEGs

Total, up-regulated and down-regulated DEGs were used for GO and KEGG analysis, respectively. GO analysis showed genes associated with BP were mainly involved in regulation of trans-synaptic signaling in total DEGs, extracellular structure organization in up-regulated DEGs, modulation of chemical synaptic transmission in down-regulated DEGs. Genes associated with CC were mainly involved in synaptic membrane in total DEGs, collagen-containing extracellular matrix in up-regulated DEGs, and synaptic membrane in down-regulated DEGs. And genes associated with MF were mainly involved in channel activity in total DEGs, extracellular matrix structural constituent in up-regulated DEGs, channel activity in down-regulated DEGs (Fig. 2A, C, E). The detailed results were presented in Table 1.

Figure 2.

Figure 2

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Functional enrichment analyses of (A) total, (C) up-regulated and (E) down-regulated DEGs (GBM vs LGG). Top 10 biological process, cellular component, and molecular function terms for the DEGs., DEGs. KEGG pathways enriched for the (B) total, (D) up-regulated and (F) down-regulated DEGs (GBM vs LGG).

Table 1.

Gene Ontology functional enrichment analyses of DEGs associated with LGG and GBM.

Category Description Count % P value
Total DEGs
 BP GO:0099177 Regulation of trans-synaptic signaling 147 6.11 4.98E-26
 BP GO:0050804 Modulation of chemical synaptic transmission 146 6.07 6.77E-26
 BP GO:0050808 Synapse organization 134 5.57 8.23E-23
 BP GO:0042391 Regulation of membrane potential 132 5.48 2.76E-19
 BP GO:0043062 Extracellular structure organization 131 5.44 6.19E-20
 CC GO:0097060 Synaptic membrane 163 6.46 1.50E-37
 CC GO:0062023 Collagen-containing extracellular matrix 143 5.67 2.25E-29
 CC GO:0043025 Neuronal cell body 140 5.55 1.53E-18
 CC GO:0045211 Postsynaptic membrane 125 4.95 6.90E-30
 CC GO:0098978 Glutamatergic synapse 123 4.88 3.15E-25
 MF GO:0015267 Channel activity 131 5.49 9.84E-16
 MF GO:0022803 Passive transmembrane transporter activity 131 5.49 1.00E-15
 MF GO:0022838 Substrate-specific channel activity 126 5.28 9.84E-16
 MF GO:0005216 Ion channel activity 123 5.15 9.84E-16
 MF GO:0046873 Metal ion transmembrane transporter activity 116 4.86 2.48E-11
Up regulated DEGs
 BP GO:0043062 Extracellular structure organization 111 8.38 1.23E-31
 BP GO:0030198 Extracellular matrix organization 106 8.01 1.25E-33
 BP GO:0042119 Neutrophil activation 101 7.63 3.85E-19
 BP GO:0002446 Neutrophil mediated immunity 100 7.55 1.19E-18
 BP GO:0043312 Neutrophil degranulation 98 7.40 1.33E-18
 CC GO:0062023 Collagen-containing extracellular matrix 121 8.75 6.04E-42
 CC GO:0005788 Endoplasmic reticulum lumen 78 5.64 1.86E-21
 CC GO:0031983 Vesicle lumen 74 5.35 1.63E-16
 CC GO:0060205 Cytoplasmic vesicle lumen 73 5.28 3.16E-16
 CC GO:0034774 Secretory granule lumen 71 5.13 2.94E-16
 MF GO:0005201 Extracellular matrix structural constituent 61 4.64 8.74E-25
 MF GO:0030246 Carbohydrate binding 46 3.50 1.09E-05
 MF GO:0005539 Glycosaminoglycan binding 45 3.42 3.37E-07
 MF GO:0061134 Peptidase regulator activity 43 3.27 6.38E-07
 MF GO:0019838 Growth factor binding 40 3.04 6.69E-12
Down regulated DEGs
 BP GO:0050804 Modulation of chemical synaptic transmission 126 11.62 9.72E-51
 BP GO:0099177 Regulation of trans-synaptic signaling 126 11.62 9.72E-51
 BP GO:0042391 Regulation of membrane potential 111 10.24 7.42E-39
 BP GO:0050808 Synapse organization 105 9.69 5.55E-37
 BP GO:0050890 Cognition 78 7.20 7.79E-28
 CC GO:0097060 Synaptic membrane 147 12.88 1.69E-71
 CC GO:0098793 Presynapse 126 11.04 5.85E-46
 CC GO:0045211 Postsynaptic membrane 115 10.08 6.77E-58
 CC GO:0098978 Glutamatergic synapse 110 9.64 2.65E-49
 CC GO:0099572 Postsynaptic specialization 107 9.38 7.56E-47
 MF GO:0015267 Channel activity 108 10.06 1.05E-33
 MF GO:0022803 Passive transmembrane transporter activity 108 10.06 1.08E-33
 MF GO:0022838 Substrate-specific channel activity 106 9.88 1.09E-34
 MF GO:0005216 Ion channel activity 105 9.79 5.81E-35
 MF GO:0046873 Metal ion transmembrane transporter activity 98 9.13 1.81E-28
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KEGG pathway enrichment analysis was conducted to further investigate the pathways among these DEGs. As shown in Figure 2B, D, F, the significantly enriched key pathways were neuroactive ligand-receptor interaction, cytokine-cytokine receptor interaction and neuroactive ligand-receptor interaction in total, up-regulated and down-regulated DEGs, respectively. The detailed results were presented in Table 2.

Table 2.

Top 10 most KEGG of total, up regulated and down regulated DEGs.

Description Count % P value
Total DEGs
 hsa04080 Neuroactive ligand-receptor interaction 86 7.51 8.47E-07
 hsa04010 MAPK signaling pathway 68 5.94 0.000258
 hsa05205 Proteoglycans in cancer 59 5.15 8.47E-07
 hsa04510 Focal adhesion 58 5.07 8.47E-07
 hsa04360 Axon guidance 51 4.45 1.64E-05
 hsa04145 Phagosome 50 4.37 1.85E-07
 hsa04020 Calcium signaling pathway 50 4.37 .000157
 hsa04514 Cell adhesion molecules (CAMs) 47 4.10 8.47E-07
 hsa04724 Glutamatergic synapse 44 3.84 1.49E-08
 hsa05322 Systemic lupus erythematosus 44 3.84 8.47E-07
Up regulated DEGs
 hsa05165 Human papillomavirus infection 32 8.24 3.63E-11
 hsa04060 Cytokine-cytokine receptor interaction 30 7.06 7.45E-09
 hsa04145 Phagosome 28 6.62 7.45E-09
 hsa04510 Focal adhesion 45 6.62 2.81E-08
 hsa05205 Proteoglycans in cancer 22 6.47 5.05E-08
 hsa05166 Human T-cell leukemia virus 1 infection 29 6.47 5.05E-08
 hsa05169 Epstein-Barr virus infection 33 6.32 5.11E-08
 hsa05322 Systemic lupus erythematosus 25 5.88 5.24E-08
 hsa05152 Tuberculosis 44 5.88 1.16E-07
 hsa04110 Cell cycle 43 4.85 2.06E-07
Down regulated DEGs
 hsa04080 Neuroactive ligand-receptor interaction 33 13.55 9.99E-17
 hsa04020 Calcium signaling pathway 41 8.82 5.36E-12
 hsa04724 Glutamatergic synapse 26 8.60 2.97E-11
 hsa04024 cAMP signaling pathway 24 8.60 1.33E-10
 hsa04723 Retrograde endocannabinoid signaling 40 7.96 7.85E-10
 hsa04727 GABAergic synapse 30 7.31 1.25E-09
 hsa05032 Morphine addiction 31 7.10 5.96E-09
 hsa04713 Circadian entrainment 20 7.10 1.09E-08
 hsa04261 Adrenergic signaling in cardiomyocytes 30 6.67 4.99E-08
 hsa04360 Axon guidance 18 6.67 6.05E-08
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3.3. Hub genes screening from PPI network

Protein interaction analysis could clarify the important protein and biological modules. PPI network construction was conducted using STRING tool and Cytoscape software. A total of 444 nodes and 1953 interactions were screened from the PPI network (Fig. 3A). CytoHubba contained several algorithms (Table 3), and the top 10 hub genes using MCC, DEGREE, and EPC algorithm were shown in Figure 3B–D. We chose the top 6 hub nodes as hub genes for further analysis, including TIMP metallopeptidase inhibitor 1 (TIMP1), fibronectin 1 (FN1), collagen type I alpha 1 chain (COL1A1), periostin (POSTN), matrix metallopeptidase 9 (MMP9), C-X-C motif chemokine ligand 8 (CXCL8).

Figure 3.

Figure 3

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PPI network analysis and hub genes identification. (A) Protein-protein interaction networks of the DEGs. The hub genes depended on (B) MMC, (C) DEGREE and (D) EPC algorithms.

Table 3.

Hub genes for DEGs ranked in cytoHubba using different methods.

Rank methods in cytoHubba
Catalog MCC MNC Degree EPC BottleNeck EcCentricity Closeness Radiality Betweenness Stress
Hub gene top 10 CXCL8 FN1 FN1 FN1 CXCL8 CXCL8 CXCL8 CXCL8 CXCL8 CXCL8
GABBR1 CXCL8 CXCL8 MMP9 FN1 FN1 FN1 FN1 FN1 FN1
HTR1A MMP9 MMP9 CXCL8 MMP9 MMP9 MMP9 MMP9 MMP9 MMP9
ANXA1 TIMP1 TIMP1 COL1A2 GRM5 GRM5 TIMP1 TIMP1 GRM5 GRM5
SAA1 COL1A1 GRIN1 TIMP1 POSTN POSTN COL1A1 GRM5 GRIN1 GRIN1
BDKRB2 COL1A2 COL1A1 COL1A1 TIMP1 TIMP1 GRM5 ANXA1 LOX GABBR1
HRH3 GRIN1 COL1A2 POSTN KCNJ9 MDM2 LOX CXCL10 KCNJ9 KCNJ9
CXCL10 POSTN POSTN COL3A1 MDM2 NTRK1 POSTN LOX TIMP1 LOX
GAL SERPINE1 LOX LOX LOX GRIN1 SERPINE1 SERPINE1 MDM2 HTR1A
SSTR2 SPP1 SERPINE1 THBS1 NTRK1 SNAI2 SPP1 SPP1 SDC1 HRH3
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3.4. Gene expression level in TCGA and GEO databases

To demonstrate the mRNA expression level in LGG and GBM, we first visualized the expression level in TCGA. As shown in Figure 4A, hub genes were significantly higher in GBM. We further analyzed 6 hub genes using GSE52009 and GSE4412 datasets. Similarly, hub gene mRNA expression levels were higher in GBM tissues, compared to LGG tissues (Fig. 4B,C).

Figure 4.

Figure 4

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Top 6 hub genes mRNA expression levels in TCGA and GEO database between LGG and GBM. (A) TCGA; (B) GSE52009; (C) GSE4412. Differences between groups were analyzed by the Student t test, ∗P < 0.05; ∗∗P < 0.01, ∗∗∗P < 0.001.

3.5. The mRNA expression levels of hub genes in Oncomine database

Oncomine, an online microarray database, was used to validate the expression levels of hub genes in LGG and GBM. There we showed six representative datasets for analyzing hub genes. As shown in Figure 5A, TIMP1 in GBM was high expression in Sun, Liang, Bredel, and Freije datasets. FN1 was more highly expressed in GBM from vandenBoom, Nutt, Sun, and Bredel datasets (Fig. 5B). Up-regulation of COL1A1 was found in GBM based on Sun, vandenBoom, Bredel, and Freije datasets (Fig. 5C). Moreover, POSTN expression level was decreased in LGG tissues from Liang, Sun, Nutt, and Freije datasets (Fig. 5D). Compared to GBM, MMP9 was reduced in LGG, demonstrated by Liang, Bredel, Nutt, and Sun datasets (Fig. 5E). As for CXCL8, Liang, vandenBoom, Freije, and Sun datasets showed high expression levels in GBM, compared to LGG (Fig. 5F).

Figure 5.

Figure 5

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Top 6 hub gene mRNA expression levels in Oncomine database. (A) TIMP1; (B) FN1; (C) COL1A1; (D) POSTN; (E) MMP9; (F) CXCL8; (G) The hub genes in human cancers, the number in the colored cell represents the number of studies meeting thresholds. The more stressed red (over-expression) or blue (under-expression) indicates a more highly significant relationship. Differences between groups were analyzed by the Student t test or one way ANOVA.

Besides, we used Oncomine dataset to analyze the hub genes expression level differences between tumor and normal tissues. As shown in Figure 5G, the database contained a total of 357, 367, 306, 367, 363, and 370 unique analyses for TIMP1, FN1, COL1A1, POSTN, MMP9, and CXCL8, respectively. In all types of tumors, TIMP1 was ranked with the top 10% of all genes showing statistically significant differences in 121 studies, with 113 studies demonstrated higher expression levels in tumor than normal tissues. Up-regulation of FN1 was found in tumor in 131 studies. 122 significant unique analyses revealed that the mRNA expression level of COL1A1 was higher in tumor. Compared to normal tissues, POSTN was up-regulated in tumors among 95 studies, while reduced expression was found in 18 studies. Higher expression of MMP9 was found in most cancers. As for CXCL8, only 67 studies were listed, and 57 studies showed high level of CXCL8 in tumor tissues. And all hub genes were up-regulated among brain and CNS cancer. Altogether, the transcriptional expression levels of hub genes were significantly up-regulated in GBM, compared to LGG and tumor tissues, compared to normal tissues.

3.6. The Kaplan–Meier plotter of hub genes

The website, http://gepia.cancer-pku.cn/, could provide the prognostic data of the hub genes based on TCGA database. And we found that expression of TIMP1 (HR=3, P = 7.8 × 10–9) was associated with worse OS for LGG patients, as well as FN1 (HR = 1.8, P = .0022), COL1A1 (HR = 2, P = .00028), POSTN (HR = 1.7, P = .0065), not MMP9 (HR = 1.4, P = .093) and CXCL8 (HR = 1.3, P = .13) (Fig. 6A–F). Only FN1 and CXCL8 expression levels were associated with worse OS for GBM patients (HR = 1.5, P = .028, HR = 1.4, P = .049), respectively. Furthermore, POSTN expression had a trend for evaluating the prognosis of GBM patients (Fig. 6G–L).

Figure 6.

Figure 6

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Kaplan-Meier plotters and log-rank tests for the prognostic value of the hub genes in LGG (A-F, A, TIMP1; B, FN1; C, COL1A1; D, POSTN; E, MMP9; F, CXCL8) and GBM (G-L, G, TIMP1; H, FN1; I, COL1A1; J, POSTN; K, MMP9; L, CXCL8).

3.7. Validation in the CGGA database

Next, we used CGGA database, which is based on a Chinese glioma patient information, to validate hub genes expression levels and prognostic value. This database has 3 datasets, including mRNAseq-325, mRNAseq-693 and mRNA array-301. mRNAseq-325 contained 325 samples which involved 182 LGG, 139 GBM and 4 unclear patients (accession number: PRJCA001746, platform: Illumina HiSeq2000 or 2500), mRNAseq-693 contained 693 samples which involved 443 LGG, 249 GBM, and 1 unclear patients (accession number: PRJCA001747, platform: Illumina HiSeq) and mRNA array-301 contained 301 patients which involved 174 LGG, 124 GBM and 2 unclear patients (platform: Agilent Whole Human Genome). We found that 6 hub genes were associated with tumor grade, except for POSTN in grade II and III (Fig. 7).

Figure 7.

Figure 7

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The hub genes mRNA expression levels in CGGA database. (A) TIMP1; (B) FN1; (C) COL1A1; (D) POSTN; (E) MMP9; (F) CXCL8. Differences between groups were analyzed by the Student t test, ∗P < 0.05; ∗∗P < 0.01, ∗∗∗P < 0.001, ns, not significant.

As shown in Figure 8, the Kaplan–Meier survival analysis showed that all 6 hub genes were significantly associated with OS, a higher expression level in patients with shorter OS. Furthermore, we evaluated the prognostic value of hub genes in different tumor grade groups. Statistically significant difference was found in patients with grade III and IV group, and no difference was found in tumor grade II group for TIMP1, as well as FN1 and MMP9 (Fig. 8A, B, E). For COL1A1, it only showed the prognostic value in patients with tumor grade II (Fig. 8C). POSTN could predict patients with tumor grade II and III, which was similar with TCGA database (Fig. 8D). The expression level of CXCL8 showed the statistically significant difference in patients with tumor grade IV, which met the same result in TCGA database (Fig. 8F).

Figure 8.

Figure 8

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Kaplan-Meier plotters and log-rank tests for the prognostic value of hub genes in CGGA database. (A) TIMP1; (B) FN1; (C) COL1A1; (D) POSTN; (E) MMP9; (F) CXCL8.

3.8. Identification of hub genes associated biological pathways

GSEA analysis was performed to determine the biologic characteristics of hub genes using TCGA database. As shown in Figure 9A, TIMP1 regulated biology process mainly associated with sugar metabolism, glutathione metabolism, and leukocyte transendothelial migration, indicating that TIMP1 might regulate metabolism and cell metastasis. Similar results were obtained in COL1A1, POSTN, MMP9, and CXCL8 group (Fig. 9C–F). It was interesting to find that FN1 might function in cell cycle-related pathways, including glycan biosynthesis, cell cycle, and cell apoptosis (Fig. 9B).

Figure 9.

Figure 9

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Signaling pathways associated with hub genes predicted by GSEA. (A) TIMP1; (B) FN1; (C) COL1A1; (D) POSTN; (E) MMP9; (F) CXCL8.

4. Discussion

The formation of tumor and the increase of malignant degree are caused by the accumulation of many gene alterations.[25] As a common malignant tumor in the brain, up to 70% of LGG transform into HGG within 10 years. This transformation process involves changes in many genes and molecular pathways.[5] This study is conducted to determine the DEGs between LGG and GBM through bioinformatics analysis. And further explore and study the molecular functional pathways involved in these DEGs, as well as their impact on tumor prognosis. Therefore, it is possible to use these DEGs as biomarkers and therapeutic targets in clinical practice, which can help the diagnosis and treatment of gliomas, and improve the prognosis of GBM.

The 5-year survival rate and overall survival time of patients with LGG and GBM are significantly different.[26] GBM grows rapidly, has high invasiveness, is easy to cause neurological symptoms, not sensitive to chemotherapy, and easy to relapse after treatment.[4] GBM can be divided into primary and secondary GBM, which are difficult to be differentiated histologically. Secondary GBM develops from LGG. Existing studies have shown that IDH mutation is a clear molecular marker for the diagnosis of secondary GBM, and it is more accurate than clinical and pathological diagnostic criteria.[7] In addition, TP53 mutation and 19q deletion are more common gene changes in secondary GBM.[8,28] Although there are some differences between primary and secondary GBM, they still have similar gene expression profiles.[27] However, there are great differences between LGG and GBM in terms of biological behavior, histology, and gene expression profiles.[28] Therefore, it is necessary to use the existing biological information analysis to further explore the differences between LGG and GBM, and help clinical diagnosis and treatment.

We used GO analysis to further clarify the functions of DEGs in biological pathways, cellular component and molecular function. Down-regulated DEGs are mainly related to the synaptic membrane structure and the activity of membrane channel, and participate in the regulation of trans-synaptic signaling. This result revealed that the down-regulated DEGs between LGG and GBM control the biological behavior of gliomas by regulating signals among different neurons. However, the up-regulated DEGs increased the malignant degree of glioblastoma mainly by changing the extracellular matrix, cytokines, and microenvironment. The above results showed that up-regulated and down-regulated DEGs changed the molecular functions, signal transduction and biological behavior of LGG and GBM through different biological pathways. The results of KEGG pathway analysis are consistent with GO analysis. The significantly enriched key pathways were cytokine-cytokine receptor interaction and neuroactive ligand-receptor interaction in up-regulated and down-regulated DEGs, respectively.

PPI network analysis was used to reveal the interactions between proteins encoded by DEGs. We identified PPI network using FC>4 to focus on DEGs with more clinical significance. Similarly, we set the cut-off criterion of interaction confidence to be greater than 0.4. The important nodes and hub DEGs in the PPI network were predicted and explored by CytoHubba. Finally, we screened out the 6 most important DEGs from PPI network analysis, and made a detailed and in-depth study on them. The 6 hub genes we focused on have been described above, as shown below: TIMP1, FN1, COL1A1, POSTN, MMP9, and CXCL8.

The expression level of TIMP1 increased significantly in malignant tumors, which can inhibit the apoptosis of tumor cells. The high expression of this gene correlates with a poor prognosis of the patients.[29] In malignant gliomas, TIMP1 is related to the decomposition of extracellular matrix and can promote the invasion and movement of tumor cells.[30] Fibronectin-1 encoded by FN1 is an important cell adhesion molecule in the extracellular matrix. Fibronectin-1 has the functions of regulating cell adhesion, growth and differentiation, promoting cell migration and proliferation, as well as ion exchange and information transfer.[31] FN1 plays an important role in the invasion and metastasis of malignant tumors, and it is also one of the current research hotspots. At present, there is a few studies focusing on FN1 and gliomas.[32] The COL1A1 gene provides instructions for making part of a large molecule called type I collagen.[33] A recent study has shown that this gene is related to the poor prognosis of malignant gliomas.[34] By knocking down of COL1A1 in invasive gliomas, the progression of tumor can be reduced and the survival rate of experimental animals can be significantly improved.[35] POSTN encodes an unstructured extracellular matrix protein called periostin. The protein can interact with multiple integrins and participate in cell proliferation, cell migration, and epithelial-mesenchymal transition.[35] This gene is expressed in the process of inflammation and many kinds of cancers, and is related to the resistance to antiangiogenic therapy and poor prognosis of gliomas.[36,37] The overexpression of MMP9 can also promote the invasion and progression of gliomas, and is related to poor prognosis.[38–40] High expression of CXCL8 in gliomas with release more cytokines, stimulate inflammation, promote cell proliferation, and lead to tumor progression.[41] The mechanism by which CXCL8 works is related to inflammatory stimulation, tumor angiogenesis, and JAK/STAT1/HIF-1α/Snail pathway activation.[42,43]

Oncomine database is one of the largest cancer gene chip databases in the world, and is committed to the data standardization and analysis of gene expression profile data of tumor samples.[17] Oncomine database can be used to analyze gene expression differences, predict co-expressed genes, and classify according to the clinical information such as tumor staging and tissue types.[44] CGGA database is the largest functional genomics database of gliomas in Asia. Its information covers matched samples of different pathological types and malignant degrees. In this study, the expression level and prognostic value of hub genes were validated by the above 2 databases.[23] Through multiple databases and multiple methods of interactive verification, we can improve the reliability and accuracy of bioinformatics analysis in this study, and reduce the research deviation caused by single database analysis.

However, there are several limitations in this study. The results above, identified in TCGA database and validated in GEO, Onocomine database, and CGGA datasets, were not validated using new tissue samples. Second, the functions of hub genes were not functionally tested, which would be conducted in our further studies.

5. Conclusion

This study carried out a systematic bioinformatics analysis of DEGs between LGG and GBM. The results provide potential biomarkers and therapeutic targets for gliomas. However, further experimental verification is needed to directly determine the role of these DEGs in glioma.

Author contributions

BX designed the study, collected the data, performed the bioinformatics analysis, analyzed the data and wrote the manuscript.

Writing – original draft: Baowei Xu.

Footnotes

Abbreviations: BP = biological pathways, CC = cellular component, CGGA = Chinese Glioma Genome Atlas, COL1A1 = collagen type I alpha 1 chain, CXCL8 = C-X-C motif chemokine ligand 8, DEG = differentially expressed gene, FDR = false discovery rate, FN1 = fibronectin 1, GBM = glioblastoma multiforme, GEO = Gene Expression Omnibus, GO = Gene Ontology, GSEA = Gene Set Enrichment Analysis, KEGG = Kyoto Encyclopedia of Genes and Genomes, LGG = low-grade glioma, MF = molecular function, MMP9 = matrix metallopeptidase 9, OS = overall survival, POSTN = periostin, PPI = protein–protein interaction, TCGA = The Cancer Genome Atlas, TIMP1 = TIMP metallopeptidase inhibitor 1.

How to cite this article: Xu B. Prediction and analysis of hub genes between glioblastoma and low-grade glioma using bioinformatics analysis. Medicine. 2021;100:3(e23513).

The datasets analyzed during the current study are available as follows:

1. The Cancer Genome Atlas (TCGA) database (https://tcgadata.nci.nih.gov/tcga/).

2. National Center of Biotechnology Information's GEO database (www.ncbi.nlm.nih.gov/geo/) with the accession numbers GSE4421 and GSE52009.

3. The online Search Tool the Retrieval of Interacting Genes (STRING) database (http://www.string-db.org).

4. The Oncomine database (https://www.oncomine.org).

5. Gene Expression Profiling Interactive Analysis (GEPIA) database (http://gepia.cancer-pku.cn/).

6. Chinese Glioma Genome Atlas (CGGA) data (http://www.cgga.org.cn/).

The author declare that he has no competing interests.

The datasets generated during and/or analyzed during the current study are publicly available.

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