Open Access

Bioinformatical analysis of gene expression signatures of different glioma subtypes

  • Authors:
    • Rui Wang
    • Jun Wei
    • Zhaohui Li
    • Yu Tian
    • Chao Du
  • View Affiliations

  • Published online on: December 20, 2017     https://doi.org/10.3892/ol.2017.7660
  • Pages: 2807-2814
  • Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

The aim of the present study was to identify the common molecular mechanisms of multiple glioma subtypes, including astrocytoma, glioblastoma and oligodendroglioma, in addition to the specific mechanisms of different types. The gene expression profile set GSE4290 was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) from three types of glioma, relative to non‑tumor tissue, were calculated by the t‑test method with a linear regression model. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the DEGs was performed. GeneVenn online analysis software was used for the comparison of the DEGs between subtypes. A total of 795 DEGs, including 619 up and 176 downregulated DEGs were screened from the astrocytoma expression profiles; these were enriched in the KEGG pathways of ‘neuroactive ligand‑receptor interaction’ (upregulated) and ‘Wnt signaling pathway’ (downregulated). Protein‑protein interaction networks for astrocytoma, glioblastoma and oligodendroglioma were constructed with 1,617, 7,027 and 1,172 pairs, respectively. A total of 595 common DEGs were obtained between the three subtypes, which were enriched in pathways associated with neural signaling. Glioblastoma is a subtype of astrocytoma; there were 195 DEGs common between these subtypes that were not also associated with oligodendroglioma. DEGs unique to astrocytoma, glioblastoma and oligodendroglioma were associated with the development of the nervous system, the cell cycle and cell matrix components, respectively. The screened DEG p53 gene is likely to be critical for glioma development, including via the Wnt and p53 signaling pathways. Brain‑derived neurotrophic factor and cyclin‑dependent kinase 1 genes were also likely to be important in the mechanism of glioma development, and were associated with the cell cycle and p53 signaling pathways. Immune system‑associated and cell matrix component pathways may be unique signaling pathways associated with astrocytoma and oligodendroglioma, respectively.

Introduction

Glioma is a type of tumor originating in the brain or spine (1). On the basis of histological features, gliomas may be divided into subtypes, including ependymoma, astrocytoma, oligodendroglioma and brainstem glioma (2). Gliomas of the brain typically induce headaches, cranial nerve disorders and seizures, whereas spinal cord gliomas induce pain and numbness in the extremities (3). Depending on the location and cell type of the disease, surgery, radiation therapy and chemotherapy may be combined in glioma treatment (4). However, gliomas are associated with a poor prognosis (5).

The underlying molecular mechanism for glioma tumorigenesis has yet to be established, as it is associated with a number of contributing oncogenes. Therefore, characterizing the molecular mechanisms of the disease is a popular area for research. Previous studies have demonstrated that polymorphisms of DNA repair genes, including excision repair cross-complementing group 1 and 2, and X-ray repair cross-complementing 1, may be associated with an increased risk of glioma development (6). Excessive DNA damage may induce the progression of cancer by causing further mutations that upregulate glioma proliferation (7). In addition, it was previously identified that microRNA-181d regulated the expression of O-6-methylguanine-DNA methyltransferase, potentially inducing glioma progression (8). Although a number of genes and microRNAs associated with glioma have been identified, it is not sufficient to establish a complete strategy for glioma treatment.

Sun et al (9) produced mRNA microarray expression profile data with tumor samples collected from glioma patients (GSE4290), which demonstrated that stem cell factor may be associated with tumor-mediated angiogenesis and the development of glioma. Using bioinformatics analysis of the Sun et al (9) study, Wei et al (10) identified additional differentially expressed genes (DEGs) and the associated transcription factors. The molecular mechanisms of different glioma subtypes were associated with distinct regulatory signaling pathways (10).

In order to research the common molecular mechanisms of gliomas, in addition to the specific mechanisms of different subtypes, the aforementioned GSE4290 gene expression profile was downloaded and analyzed in the present study. A DEG comparison between different subtypes was performed. This may lay the theoretical foundation for novel strategies of glioma treatment.

Materials and methods

Data acquisition

The gene expression profile collection GSE4290 (9), which included the expression profile data from 180 samples, was downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). The data had been generated using the GPL570 (HG-U133_Plus_2) Affymetrix Human Genome U133 Plus 2.0 microarray platform. The data of 23 samples from the glial cells of epilepsy patients from GSE4290 were used as non-tumor control profiles. The remaining 157 tumor expression profiles included 26 astrocytoma profiles, 50 oligodendroglioma profiles and 81 glioblastoma profiles. The raw data were obtained for the subsequent analysis.

Data preprocessing and DEG screening

The reduced major axis method (11) was used to normalize the raw data with the Affy package (12) in R. Compared with non-tumor expression profiles, the DEGs from each glioma subtype were identified by the T-test method with a linear regression model from the R package limma (13). The threshold for DEGs was |logFC| >1.0 and P<0.05.

Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs

The GO database comprises data concerning gene annotations, which primarily includes 3 categories: Molecular function (MF); biological process (BP); and cellular component (CC) (14). KEGG (www.kegg.jp) is a database for the systematic analysis of gene functions. The online tool Database for Annotation, Visualization and Integrated Discovery (DAVID) (15) was used for a KEGG pathway enrichment analysis of the identified DEGs. P<0.05 was considered to indicate a significant enrichment.

Protein-protein interaction (PPI) network construction

STRING is a database of experimentally confirmed and predicted PPIs (16). A PPI network was constructed based on STRING and visualized with Cytoscape 2.8.2 (17) with the threshold of combined score >0.4. The degree of connectivity was used to identify hub nodes and remove nodes of low significance.

Module analysis and KEGG enrichment analysis

Modules, i.e., groups of genes with similar functional properties, of the constructed PPI network were identified with ClusterONE (18) in Cytoscape with a threshold of P<0.05. The DEG modules were subsequently used for KEGG pathway enrichment analysis as previously described.

DEG comparison of different subtypes

GeneVenn is an online application for comparing gene lists using Venn diagrams (19). GeneVenn software was used for comparing DEGs between the glioma subtypes.

Results

DEG screening and pathway enrichment analysis
Astrocytoma

Compared with non-tumor expression profiles, a total of 863 DEGs, including 624 upregulated and 239 downregulated DEGs, were screened from the astrocytoma expression profile data. The upregulated DEGs were enriched in KEGG pathways including ‘neuroactive ligand-receptor interaction’, ‘calcium signaling pathway’, ‘MAPK signaling pathway’ and ‘gap junction’, whereas downregulated DEGs were enriched in pathways including ‘cell adhesion molecules’, ‘complement and coagulation cascades’ and ‘intestinal immune network for IgA production’ (Table I).

Table I.

Top 10 pathways associated with upregulated and downregulated DEGs in astrocytoma expression profiles.

Table I.

Top 10 pathways associated with upregulated and downregulated DEGs in astrocytoma expression profiles.

TermDEGsP-value
Upregulated pathways
  hsa04080: Neuroactive ligand-receptor interaction29 1.57×10−9
  hsa04020:Calcium signaling pathway22 4.84×10−8
  hsa04010:MAPK signaling pathway22 4.43×10−5
  hsa04540:Gap junction12 6.55×10−5
  hsa04360:Axon guidance14 1.23×10−4
  hsa04720:Long-term potentiation10 1.81×10−4
  hsa04012:ErbB signaling pathway11 2.61×10−4
  hsa04730:Long-term depression  8 4.56×10−3
  hsa05014:Amyotrophic lateral sclerosis  6 2.13×10−2
  hsa04666:FcγR-mediated phagocytosis  8 2.43×10−2
Downregulated pathways
  hsa04514:Cell adhesion molecules  9 1.28×10−3
  hsa05222:Small cell lung cancer  7 2.32×10−3
  hsa04610:Complement and coagulation cascades  6 5.09×10−3
  hsa04672:Intestinal immune network for IgA production  5 8.01×10−3
  hsa04310:Wnt signaling pathway  8 1.11×10−2
  hsa05216:Thyroid cancer  4 1.13×10−2
  hsa05310:Asthma  4 1.13×10−2
  hsa05217:Basal cell carcinoma  5 1.20×10−2
  hsa05020:Prion diseases  4 1.89×10−2
  hsa05330:Allograft rejection  4 2.03×10−2

[i] DEG, differentially expressed gene.

Glioblastoma

There were 1,520 DEGs, including 969 upregulated and 551 downregulated DEGs, between non-tumor and glioblastoma expression profiles. Upregulated DEGs were enriched in KEGG pathways including ‘calcium signaling pathway’, ‘long-term potentiation’, ‘neuroactive ligand-receptor interaction’, ‘MAPK signaling pathway’ and ‘axon guidance’, whereas downregulated DEGs were associated with the pathways of ‘cell cycle’, ‘ECM-receptor interaction’, ‘complement and coagulation cascades’, ‘focal adhesion’ and ‘p53 signaling pathway’ (Table II).

Table II.

Top 10 pathways associated with up- and downregulated DEGs in glioblastoma expression profiles.

Table II.

Top 10 pathways associated with up- and downregulated DEGs in glioblastoma expression profiles.

TermDEGP-valueGenes
Upregulated
  hsa04020:Calcium signaling pathway30 1.25×10−9DRD1, CAMK2G, PPP3R1, ITPKA, ATP2B1, ATP2B2, PDE1A, PPP3CB, CAMK2B, PPP3CA, PRKACB, CAMK2A, SLC8A2, SLC25A4, GRIN1, GRIN2A, PRKCG, ITPR1, PRKCB, GRM5, GNAL, CAMK4, CHRM3, CHRM1, RYR1, RYR2, CACNA1E, HTR2C, HTR2A, CACNA1B
  hsa04720:Long-term potentiation18 6.56×10−9MAP2K1, CAMK2G, GRIN1, GRIN2A, PPP3R1, PRKCG, ITPR1, PRKCB, GRM5, CAMK4, GRIA2, GRIA1, PPP1R1A, PPP3CB, CAMK2B, PRKACB, PPP3CA, CAMK2A
  hsa04080:Neuroactive ligand-receptor interaction34 5.04×10−8GPR83, DRD1, THRB, GABRB3, GABRB2, GABRB1, OPRK1, GABBR2, LPAR1, VIPR1, PRSS3, ADRA2A, GABRG1, GABRD, GABRG2, GABRA2, GLRB, GABRA1, GABRA4, RXFP1, GABRA5, GRIN1, GRIN2A, NPY1R, NTSR2, GRM5, GRM3, CHRM3, GRIA2, SSTR1, GRIA1, CHRM1, HTR2C, HTR2A
  hsa04010:MAPK signaling pathway33 4.55×10−7MEF2C, FGF9, PPP3R1, FGF13, CACNB3, FGF12, ACVR1C, CDC42, BDNF, HSPA2, RASGRP1, PPP3CB, PPP3CA, PRKACB, PAK1, CACNA2D1, MAP2K1, NLK, PTPN5, MAP2K4, PTPRR, PRKCG, CACNG3, CACNG2, MAPK10, CACNA2D3, PRKCB, RASGRF2, ARRB1, MAPK8IP2, MAPK9, CACNA1E, CACNA1B
  hsa04730:Long-term depression14 1.26×10−5GNAZ, GNAO1, MAP2K1, GNAI1, PRKCG, ITPR1, PRKCB, GRM5, GRIA2, GRIA1, RYR1, CRH, GUCY1A3, GUCY1B3
  hsa04360:Axon guidance18 8.09×10−5NGEF, GNAI1, NTN4, PPP3R1, L1CAM, SLIT2, PAK6, CDC42, PAK7, EPHB6, RND1, UNC5A, PAK3, PPP3CB, UNC5D, SEMA4D, PAK1, PPP3CA
  hsa05014:Amyotrophic lateral sclerosis11 1.28×10−4SLC1A2, GRIA2, GRIA1, GRIN1, PPP3CB, PPP3R1, GRIN2A, NEFH, PPP3CA, NEFL, NEFM
  hsa04012:ErbB signaling pathway14 1.59×10−4NRG3, MAP2K1, CAMK2G, MAP2K4, PRKCG, MAPK10, PRKCB, PAK6, PAK7, PAK3, MAPK9, CAMK2B, PAK1, CAMK2A
  hsa04540:Gap junction14 2.01×10−4DRD1, MAP2K1, GNAI1, PRKCG, LPAR1, ITPR1, PRKCB, GRM5, GUCY1A3, TUBA4A, GUCY1B3, PRKACB, HTR2C, HTR2A
  hsa04310:Wnt signaling pathway16 4.05×10−3NLK, CAMK2G, PPP3R1, PRKCG, MAPK10, DAAM2, PRKCB, SFRP2, PRICKLE2, PPP3CB, MAPK9, WIF1, CAMK2B, PRKACB, PPP3CA, CAMK2A
Downregulated
  hsa04110: Cell cycle22 8.93×10−9CDK1, DBF4, TP53, TTK, CDC20, MCM2, PTTG1, CDK4, MCM3, MCM5, WEE1, TGFB2, CCNB1, MCM7, MAD2L1, CCND2, CDKN2C, PCNA, BUB1B, CCNA2, GADD45A, MYC
  hsa04512: ECM-receptor interaction16 5.84×10−7IBSP, COL4A2, COL4A1, TNC, COL3A1, COL5A2, LAMB2, CD44, ITGA7, COL6A3, COL1A2, COL6A2, LAMC1, COL1A1, LAMB1, FN1
  hsa04610:Complement and coagulation cascades14 1.80×10−6PLAT, C5AR1, C3, SERPING1, C1R, C1S, C1QC, C1QA, C1QB, SERPINE1, CFI, PROS1, PLAU, F2R
  hsa04510:Focal adhesion23 8.48×10−6EGFR, IBSP, CAV1, COL4A2, COL4A1, TNC, COL3A1, COL5A2, FLNA, LAMB2, CCND2, VEGFA, ITGA7, COL6A3, COL1A2, COL6A2, SHC1, PDGFC, COL1A1, LAMC1, ZYX, LAMB1, FN1
  hsa04115:p53 signaling pathway11 2.58×10−4STEAP3, CCNB1, CDK1, TP53I3, CCND2, RRM2, SERPINE1, TP53, CDK4, IGFBP3, GADD45A
  hsa04612:Antigen processing and presentation11 1.30×10−3TAP1, HLA-A, HSPA6, IFI30, HLA-C, HLA-DPA1, HLA-B, HLA-DMA, RFXANK, HLA-G, HLA-DRA, HLA-F
  hsa05330:Allograft rejection  7 2.37×10−3HLA-A, HLA-C, HLA-DPA1, HLA-B, HLA-DMA, HLA-G, HLA-DRA, HLA-F
  hsa03030:DNA replication  7 2.37×10−3MCM7, RFC4, PCNA, MCM2, MCM3, RNASEH2A, MCM5
  hsa05332:Graft-versus-host disease  7 3.60×10−3HLA-A, HLA-C, HLA-DPA1, HLA-B, HLA-DMA, HLA-G, HLA-DRA, HLA-F
  hsa04940:Type I diabetes mellitus  7 5.25×10−3HLA-A, HLA-C, HLA-DPA1, HLA-B, HLA-DMA, HLA-G, HLA-DRA, HLA-F

[i] DEG, differentially expressed gene.

Oligodendroglioma

Compared with the non-tumor expression profiles, a total of 795 DEGs, including 619 upregulated and 176 downregulated DEGs, were screened from the astrocytoma expression profile data. The upregulated DEGs were enriched in ‘neuroactive ligand-receptor interaction’, ‘calcium signaling pathway’, ‘axon guidance’ and ‘gap junction’, whereas downregulated DEGs were enriched in ‘TGF-β signaling pathway’, ‘p53 signaling pathway’ and ‘Wnt signaling pathway’ (Table III).

Table III.

Top 10 pathways of up- and downregulated DEGs in oligodendroglioma expression profiles.

Table III.

Top 10 pathways of up- and downregulated DEGs in oligodendroglioma expression profiles.

TermDEGsP-valueGenes
Upregulated
  hsa04080:Neuroactive ligand-receptor interaction27 4.15×10−8GPR83, DRD1, THRB, GABRB2, GABRB1, GABBR2, LPAR1, VIPR1, KISS1R, PRSS3, GABRG1, GABRD, GABRG2, GABRA2, GLRB, GABRA1, GABRA4, RXFP1, GRIN1, GABRA5, GRIN2A, NPY1R, GRM3, CHRM3, CHRM1, HTR2C, HTR2A
  hsa04020:Calcium signaling pathway20 1.32×10−6DRD1, SLC8A2, GRIN1, GRIN2A, PPP3R1, PRKCG, ITPKA, ITPR1, PRKCB, ATP2B1, CHRM3, RYR3, CHRM1, PDE1A, RYR2, CAMK2B, HTR2C, CAMK2A, HTR2A, CACNA1B
  hsa04540:Gap junction12 7.37×10−5DRD1, MAP2K1, GNAI1, TUBB2A, TUBA4A, GUCY1B3, PRKCG, LPAR1, HTR2C, ITPR1, PRKCB, HTR2A
  hsa04360:Axon guidance14 1.40×10−4NGEF, GNAI1, PPP3R1, SLIT2, PAK6, CDC42, EPHA4, PAK7, EPHB6, PAK3, SEMA3E, UNC5D, PAK1, SEMA4D
  hsa04720:Long-term potentiation9 1.02×10−3MAP2K1, GRIN1, PPP3R1, GRIN2A, PRKCG, CAMK2B, CAMK2A, ITPR1, PRKCB
  hsa04010:MAPK signaling pathway19 1.23×10−3MEF2C, MAP2K1, PTPN5, MAP2K4, PPP3R1, PTPRR, FGF13, PRKCG, CACNG3, CACNB3, CACNA2D3, ACVR1C, PRKCB, CDC42, BDNF, HSPA2, RASGRF2, PAK1, CACNA1B
  hsa04012:ErbB signaling pathway10 1.27×10−3PAK6, PAK7, MAP2K1, PAK3, MAP2K4, PRKCG, CAMK2B, PAK1, CAMK2A, PRKCB
  hsa04666:FcγR-mediated phagocytosis10 2.35×10−3CDC42, MAP2K1, PPAP2C, WASF1, PRKCG, PAK1, PRKCD, DNM1, PRKCB, AMPH
  hsa05014:Amyotrophic lateral sclerosis  6 2.24×10−2GRIN1, PPP3R1, GRIN2A, NEFH, NEFL, NEFM
  hsa04912:GnRH signaling pathway  8 3.01×10−2CDC42, MAP2K1, MAP2K4, CAMK2B, CAMK2A, PRKCD, ITPR1, PRKCB
Downregulated
  hsa04350:TGF-beta signaling pathway  8 3.11×10−5AMH, NOG, BMP2, ID1, SMAD5, ID4, ID3, MYC
  hsa04115:p53 signaling pathway  6 7.02×10−4BID, CCND1, RRM2, GADD45G, TP53, CDK4
  hsa05216:Thyroid cancer  4 3.40×10−3CCND1, TP53, MYC, TCF7L1
  hsa04310:Wnt signaling pathway  7 4.94×10−3CCND1, VANGL2, TP53, MYC, TCF7L1, PRKX, FZD7
  hsa05219:Bladder cancer  4 9.70×10−3CCND1, TP53, CDK4, MYC
  hsa04110:Cell cycle  6 1.00×10−2CCND1, MCM7, GADD45G, TP53, CDK4, MYC
  hsa05210:Colorectal cancer  5 1.17×10−2CCND1, TP53, MYC, TCF7L1, FZD7
  hsa05213:Endometrial cancer  4 1.73×10−2CCND1, TP53, MYC, TCF7L1
  hsa05217:Basal cell carcinoma  4 2.01×10−2BMP2, TP53, TCF7L1, FZD7
  hsa05212:Pancreatic cancer  4 4.04×10−2CCND1, ARHGEF6, TP53, CDK4

[i] DEG, differentially expressed gene.

PPI network construction and module analysis
Astrocytoma

With the threshold of combined score >0.4, a PPI network for astrocytoma was constructed with 1,617 pairs. Once nodes with a degree <2 were removed, a PPI network for astrocytoma with 506 nodes and 1,590 edges was obtained. In this network, the hub nodes with a degree score >25 were SPY, tumor protein p53 (TP53), brain-derived neurotrophic factor (BDNF), NPY, SST, TAC1 and SYT1. Module analysis was subsequently performed for this PPI network. Modules A-C were screened, with P=2.065×10−8, P=3.418×10−7 and P=7.808×10−4, respectively. Module A included 24 nodes and 126 edges; module B included 21 nodes and 120 edges; module C included 10 nodes and 31 edges (Fig. 1A). On the basis of the analysis of modules A-C, 8 genes in these modules were enriched in the ‘neuroactive ligand-receptor interaction’ pathway.

Glioblastoma

A total of 7,027 pairs were identified in the PPI network for glioblastoma. Once nodes with a degree <2 were removed, a PPI network with 1,064 nodes and 7,003 edges was obtained. Hub nodes with a degree score >90 were cyclin-dependent kinase 1 (CDK1), PCNA, TP53, KNTC1 and CCNB1. A total of 4 modules were screened with P<0.05; modules D-G were screened with P<0.001. Module D included 27 nodes and 178 edges, module E included 27 nodes and 176 edges, module F included 12 nodes and 33 edges (Fig. 1B), and module G included 7 nodes and 11 edges. Genes in modules D-F were enriched in the ‘protein processing in endoplasmic reticulum’ pathway (P=1.13×10−16).

Oligodendroglioma

A total of 1,172 pairs were identified in the PPI network for oligodendroglioma. Once nodes with a degree <2 were removed, a PPI network with 419 nodes and 1,040 edges was obtained. SPY, TP53, BDNF, CDC42, SYN1, TAC1, NPY, SYT1, SNAP25, MCM7 and ENO2 were identified as hub nodes, with a degree score >20. With the threshold of P<0.05, only module H was screened. Module H was associated with P<0.001. Module H contained 22 nodes and 108 edges (Fig. 1C). The genes in module H were associated with the pathways of ‘neuroactive ligand-receptor interaction’ (P=3.20×10−14) and ‘calcium signaling pathway’ (P=7.75×10−10).

DEGs comparison of different subtype

As included in Table IV, a total of 595 common DEGs were obtained across all three subtypes of glioma (Fig. 2). The pathways enriched with these genes were associated with neural signaling. Furthermore, glioblastoma is a subtype of astrocytoma; there were 195 common DEGs between the glioblastoma and astrocytoma datasets that were not also associated with oligodendroglioma, which were enriched for immune function-associated pathways. The unique DEGs from astrocytoma, glioblastoma and oligodendroglioma were generally associated with the development of the nervous system, the cell cycle and cell matrix components, respectively (Table IV).

Table IV.

GO term enrichment analysis of unique DEGs in three types of glioma.

Table IV.

GO term enrichment analysis of unique DEGs in three types of glioma.

A, Astrocytoma (enrichment score, 2)

GO categoryGO termDEGsP-value
BP GO:0050767:Regulation of neurogenesis  3 7.66×10−3
BP GO:0051960:Regulation of nervous system development  3 1.01×10−2
BP GO:0060284:Regulation of cell development  3 1.15×10−2
BPGO:0045596:Negative regulation of cell differentiation  3 1.27×10−2

B, Glioblastoma (enrichment score, 7)

GO categoryGO termDEGsP-value

BPGO:0022403:Cell cycle phase44 4.80×10−10
BPGO:0000278:Mitotic cell cycle40 2.09×10−9
BPGO:0022402:Cell cycle process50 1.26×10−8
BPGO:0000280:Nuclear division27 1.14×10−7
BP GO:0007067:Mitosis27 1.14×10−7
CC GO:0005819:Spindle22 1.29×10−7
BPGO:0000087:M phase of mitotic cell cycle27 1.64×10−7
BP GO:0048285:Organelle fission27 2.54×10−7
BPGO:0007049:Cell cycle58 2.63×10−7
BPGO:0051301:Cell division31 3.48×10−7
BPGO:0000279:M phase33 3.88×10−7
CC GO:0015630:Microtubule cytoskeleton38 3.98×10−4

C, Oligodendroglioma (enrichment score, 2)

GO categoryGO termDEGsP-value

CC GO:0044421:Extracellular region part12 1.40×10−3
CC GO:0005576:Extracellular region16 1.19×10−2
CC GO:0005615:Extracellular space  8 2.02×10−2

[i] GO, Gene Ontology; DEG, differentially expressed gene; BP, biological process; CC, cellular component.

Discussion

In order to screen for potential therapeutic targets in different glioma subtypes, the GSE4290 profile was downloaded from the GEO for a bioinformatics analysis of the associated molecular mechanisms. In the present study, a total of 595 common DEGs were identified between the three glioma subtypes. The pathways enriched by these genes were associated with neural signaling. There were also a number of unique DEGs and pathways specifically associated with different subtypes.

TP53 was screened as an overlapped DEG between the three glioma subtypes. Additionally, it was enriched in various pathways including the Wnt signaling pathway and the p53 signaling pathway. TP53 is a critical target in the regulation of malignant progenitor cell renewal, differentiation and tumorigenic potential (20). In addition, cellular pathways involving TP53 are frequently dysregulated in glioma tumors (21). Dickkopf-1 was previously demonstrated to be an inhibitor of the Wnt signaling pathway by inducing TP53 tumor suppression (22). Dysregulation of the TP53 pathway was also necessary for human astrocytoma by regulating the G1-S transition (23). Therefore, alterations to TP53 expression are critical in glioma via the Wnt and p53 signaling pathways.

Compared with non-tumor expression profiles, notable genes, including BDNF, were screened from the astrocytoma expression profiles, which were enriched in the KEGG pathways of ‘cell adhesion molecules’, ‘complement and coagulation cascades’ and ‘Wnt signaling pathway’. BDNF, a member of the nerve growth factor family, is necessary for the survival of striatal neurons in the brain; in human glioma, the expression of BDNF was previously demonstrated to be upregulated and closely associated with pathological grading (24). In addition, Xiong et al (25) identified that mature BDNF could promote the growth of glioma cells in vitro. The expression of BDNF was confirmed to be regulated by the Wnt signaling pathway (25). Therefore, BDNF may be a therapeutic target in astrocytoma.

CDK1 was a hub node of the PPI network for glioblastoma expression profiles. Chen et al (26) identified that the overexpression of CDK1 may have promoted the oncogenesis and progression of glioma, whereas the downregulation of CDK1 inhibited proliferation. Combined with cyclin B1, CDK1 forms a complex that induces the G2-M transition in malignant glioma cells (27). In the present study, CDK1 was associated with the KEGG pathways ‘cell cycle’ and ‘p53 signaling pathway’. For the treatment of human glioblastoma cells, inducing G1 cell cycle arrest, as may be mediated by the p53 pathway, is an effective strategy for suppressing tumorigenicity (28). CDK1 may thus be associated with the mechanisms of glioblastoma by affecting the cell cycle and the p53 signaling pathway.

In the present study, pathways enriched by DEGs common between the three types of glioma were associated with neural signaling. The unique genes of astrocytoma and oligodendroglioma were enriched in immune- and cell matrix component-associated pathways, respectively. The simultaneous activation of the Ras and Akt pathways has been demonstrated to induce glioblastoma development in mice (29). Alterations to the immune system were previously observed to be the primary etiology of adult glioma, particularly in the brain (30). In the process of tumor invasion, extracellular matrix proteins, including fibronectin, may also serve an important function in intracerebral invasion (31).

In conclusion, the screened DEG TP53 is likely to be critical for glioma development, including via the Wnt and p53 signaling pathways. BDNF and CDK1 were also possibly important in the mechanism of glioma development, and were associated with the cell cycle and p53 signaling pathways. Immune system-associated and cell matrix component pathways may be unique signaling pathways associated with astrocytoma and oligodendroglioma, respectively. However, further experiments are required to confirm the results of the present study.

References

1 

Hori M, Fukunaga I, Masutani Y, Taoka T, Kamagata K, Suzuki Y and Aoki S: Visualizing non-Gaussian diffusion: Clinical application of q-space imaging and diffusional kurtosis imaging of the brain and spine. Magn Reson Med Sci. 11:221–233. 2012. View Article : Google Scholar : PubMed/NCBI

2 

Chan AS, Leung SY, Wong MP, Yuen ST, Cheung N, Fan YW and Chung LP: Expression of vascular endothelial growth factor and its receptors in the anaplastic progression of astrocytoma, oligodendroglioma, and ependymoma. Am J Surg Pathol. 22:816–826. 1998. View Article : Google Scholar : PubMed/NCBI

3 

Pickuth D and Heywang-Köbrunner SH: Neurosarcoidosis: Evaluation with MRI. J Neuroradiol. 27:185–188. 2000.PubMed/NCBI

4 

Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJ, Belanger K, Brandes AA, Marosi C, Bogdahn U, et al: Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med. 352:987–996. 2005. View Article : Google Scholar : PubMed/NCBI

5 

Bruna A, Darken RS, Rojo F, Ocaña A, Peñuelas S, Arias A, Paris R, Tortosa A, Mora J, Baselga J and Seoane J: High TGFbeta-Smad activity confers poor prognosis in glioma patients and promotes cell proliferation depending on the methylation of the PDGF-B gene. Cancer Cell. 11:147–160. 2007. View Article : Google Scholar : PubMed/NCBI

6 

Wrensch M, Kelsey KT, Liu M, Miike R, Moghadassi M, Sison JD, Aldape K, McMillan A, Wiemels J and Wiencke JK: ERCC1 and ERCC2 polymorphisms and adult glioma. Neuro Oncol. 7:495–507. 2005. View Article : Google Scholar : PubMed/NCBI

7 

Gurung RL, Lim SN, Khaw AK, Soon JF, Shenoy K, Mohamed Ali S, Jayapal M, Sethu S, Baskar R and Hande MP: Thymoquinone induces telomere shortening, DNA damage and apoptosis in human glioblastoma cells. PLoS One. 5:e121242010. View Article : Google Scholar : PubMed/NCBI

8 

Zhang W, Zhang J, Hoadley K, Kushwaha D, Ramakrishnan V, Li S, Kang C, You Y, Jiang C, Song SW, et al: miR-181d: A predictive glioblastoma biomarker that downregulates MGMT expression. Neuro Oncol. 14:712–719. 2012. View Article : Google Scholar : PubMed/NCBI

9 

Sun L, Hui AM, Su Q, Vortmeyer A, Kotliarov Y, Pastorino S, Passaniti A, Menon J, Walling J, Bailey R, et al: Neuronal and glioma-derived stem cell factor induces angiogenesis within the brain. Cancer Cell. 9:287–300. 2006. View Article : Google Scholar : PubMed/NCBI

10 

Wei B, Wang L, Du C, Hu G, Wang L, Jin Y and Kong D: Identification of differentially expressed genes regulated by transcription factors in glioblastomas by bioinformatics analysis. Mol Med Rep. 11:2548–2554. 2015. View Article : Google Scholar : PubMed/NCBI

11 

Bohonak AJ and van der Linde K: RMA: Software for reduced major axis regression, Java version. 2004.http://www.kimvdlinde.com/professional/rma.html

12 

Gautier L, Cope L, Bolstad BM and Irizarry RA: Affy-analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 20:307–315. 2004. View Article : Google Scholar : PubMed/NCBI

13 

Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W and Smyth GK: Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43:e472015. View Article : Google Scholar : PubMed/NCBI

14 

Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al: Gene Ontology: Tool for the unification of biology. The gene ontology consortium. Nat Genet. 25:25–29. 2000. View Article : Google Scholar : PubMed/NCBI

15 

Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC and Lempicki RA: DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol. 4:P32003. View Article : Google Scholar : PubMed/NCBI

16 

Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, et al: STRING v10: Protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43(Database issue): D447–D452. 2015. View Article : Google Scholar : PubMed/NCBI

17 

Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI

18 

Nepusz T, Yu H and Paccanaro A: Detecting overlapping protein complexes in protein-protein interaction networks. Nat Methods. 9:471–472. 2012. View Article : Google Scholar : PubMed/NCBI

19 

Pirooznia M, Nagarajan V and Deng Y: GeneVenn-A web application for comparing gene lists using Venn diagrams. Bioinformation. 1:420–422. 2007. View Article : Google Scholar : PubMed/NCBI

20 

Zheng H, Ying H, Yan H, Kimmelman AC, Hiller DJ, Chen AJ, Perry SR, Tonon G, Chu GC, Ding Z, et al: p53 and Pten control neural and glioma stem/progenitor cell renewal and differentiation. Nature. 455:1129–1133. 2008. View Article : Google Scholar : PubMed/NCBI

21 

Ishii N, Maier D, Merlo A, Tada M, Sawamura Y, Diserens AC and Van Meir EG: Frequent co-alterations of TP53, p16/CDKN2A, p14ARF, PTEN tumor suppressor genes in human glioma cell lines. Brain Pathol. 9:469–479. 1999. View Article : Google Scholar : PubMed/NCBI

22 

Wang J, Shou J and Chen X: Dickkopf-1, an inhibitor of the Wnt signaling pathway, is induced by p53. Oncogene. 19:1843–1848. 2000. View Article : Google Scholar : PubMed/NCBI

23 

Ichimura K, Bolin MB, Goike HM, Schmidt EE, Moshref A and Collins VP: Deregulation of the p14ARF/MDM2/p53 pathway is a prerequisite for human astrocytic gliomas with G1-S transition control gene abnormalities. Cancer Res. 60:417–424. 2000.PubMed/NCBI

24 

Yan Q, Yu HL and Li JT: Study on the expression of BDNF in human gliomas. Sichuan Da Xue Xue Bao Yi Xue Ban. 40:415–417. 2009.(In Chinese). PubMed/NCBI

25 

Xiong J, Zhou L, Lim Y, Yang M, Zhu YH, Li ZW, Zhou FH, Xiao ZC and Zhou XF: Mature BDNF promotes the growth of glioma cells in vitro. Oncol Rep. 30:2719–2724. 2013. View Article : Google Scholar : PubMed/NCBI

26 

Chen H, Huang Q, Zhai DZ, Dong J, Wang AD and Lan Q: CDK1 expression and effects of CDK1 silencing on the malignant phenotype of glioma cells. Zhonghua Zhong Liu Za Zhi. 29:484–488. 2007.(In Chinese). PubMed/NCBI

27 

Liu WT, Chen C, Lu IC, Kuo SC, Lee KH, Chen TL, Song TS, Lu YL, Gean PW and Hour MJ: MJ-66 induces malignant glioma cells G2/M phase arrest and mitotic catastrophe through regulation of cyclin B1/Cdk1 complex. Neuropharmacology. 86:219–227. 2014. View Article : Google Scholar : PubMed/NCBI

28 

Medema RH, Kops GJ, Bos JL and Burgering BM: AFX-like Forkhead transcription factors mediate cell-cycle regulation by Ras and PKB through p27kip1. Nature. 404:782–787. 2000. View Article : Google Scholar : PubMed/NCBI

29 

Holland EC, Celestino J, Dai C, Schaefer L, Sawaya RE and Fuller GN: Combined activation of Ras and Akt in neural progenitors induces glioblastoma formation in mice. Nat Genet. 25:55–57. 2000. View Article : Google Scholar : PubMed/NCBI

30 

Rajaraman P, Brenner AV, Butler MA, Wang SS, Pfeiffer RM, Ruder AM, Linet MS, Yeager M, Wang Z, Orr N, et al: Common variation in genes related to innate immunity and risk of adult glioma. Cancer Epidemiol Biomarkers Prev. 18:1651–1658. 2009. View Article : Google Scholar : PubMed/NCBI

31 

Enam SA, Rosenblum ML and Edvardsen K: Role of extracellular matrix in tumor invasion: Migration of glioma cells along fibronectin-positive mesenchymal cell processes. Neurosurgery. 42:599–608. 1998. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

March-2018
Volume 15 Issue 3

Print ISSN: 1792-1074
Online ISSN:1792-1082

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
x
Spandidos Publications style
Wang R, Wei J, Li Z, Tian Y and Du C: Bioinformatical analysis of gene expression signatures of different glioma subtypes. Oncol Lett 15: 2807-2814, 2018
APA
Wang, R., Wei, J., Li, Z., Tian, Y., & Du, C. (2018). Bioinformatical analysis of gene expression signatures of different glioma subtypes. Oncology Letters, 15, 2807-2814. https://doi.org/10.3892/ol.2017.7660
MLA
Wang, R., Wei, J., Li, Z., Tian, Y., Du, C."Bioinformatical analysis of gene expression signatures of different glioma subtypes". Oncology Letters 15.3 (2018): 2807-2814.
Chicago
Wang, R., Wei, J., Li, Z., Tian, Y., Du, C."Bioinformatical analysis of gene expression signatures of different glioma subtypes". Oncology Letters 15, no. 3 (2018): 2807-2814. https://doi.org/10.3892/ol.2017.7660