Open Access

Identification of key genes and pathways in meningioma by bioinformatics analysis

  • Authors:
    • Junxi Dai
    • Yanbin Ma
    • Shenghua Chu
    • Nanyang Le
    • Jun Cao
    • Yang Wang
  • View Affiliations

  • Published online on: March 29, 2018     https://doi.org/10.3892/ol.2018.8376
  • Pages: 8245-8252
  • Copyright: © Dai 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

Meningioma is the most frequently occurring type of brain tumor. The present study aimed to conduct a comprehensive bioinformatics analysis of key genes and relevant pathways involved in meningioma, and acquire further insight into the underlying molecular mechanisms. Initially, differentially expressed genes (DEGs) in 47 meningioma samples as compared with 4 normal meninges were identified. Subsequently, these DEGs were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. In addition, a protein‑protein interaction (PPI) network of the identified DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes and visualized using Cytoscape. In total, 1,683 DEGs were identified, including 66 upregulated and 1,617 downregulated genes. The GO analysis results revealed that the DEGs were significantly associated with the ‘protein binding’, ‘cytoplasm’, ‘extracellular matrix (ECM) organization’ and ‘cell adhesion’ terms. The KEGG analysis results demonstrated the significant pathways included ‘AGE‑RAGE signaling pathway in diabetic complications’, ‘PI3K‑Akt signaling pathway’, ‘ECM‑receptor interaction’ and ‘cell adhesion molecules’. The top five hub genes obtained from the PPI network were JUN, PIK3R1, FOS, AGT and MYC, and the most enriched KEGG pathways associated with the four obtained modules were ‘chemokine signaling pathway’, ‘cytokine‑cytokine receptor interaction’, ‘allograft rejection’, and ‘complement and coagulation cascades’. In conclusion, bioinformatics analysis identified a number of potential biomarkers and relevant pathways that may represent key mechanisms involved in the development and progression of meningioma. However, these findings require verification in future experimental studies.

Introduction

Meningiomas are common intracranial tumors that account for ~36% of all primary central nervous system tumors (1). According to the World Health Organization classification (2), meningiomas may be divided into three grades, including benign (Grade I), atypical (Grade II) and anaplastic (Grade III) meningiomas. Although the majority of meningiomas are benign tumors that are curable by surgery, atypical and anaplastic tumors remain therapeutically challenging due to the high risk of tumor relapse (3,4). Furthermore, even after complete resection, relapse occurs in >5% of benign meningiomas (5,6).

The pathogenesis of meningioma is a complex process associated with an accumulation of various genetic and epigenetic alterations that occur during the initiation and progression of the tumor (7). Monosomy 22, 22q deletion and/or mutation of the neurofibromatosis type 2 gene have been identified as important initiating events and represent the most common genetic alterations in meningiomas (810). Other common chromosomal alterations include deletions of 1p, 6q, 10q and 14q, and insertions of 1q, 9q, 12q, 15q, 17q and 20q (7,11,12). However, there is insufficient evidence to verify the capability of these chromosomal alterations to predict tumor recurrence and progression.

Several gene expression profiling studies have been conducted on meningiomas, and several candidate genes have been proposed as recurrence-associated predictors or progression-associated biomarkers of meningiomas among the differentially expressed genes (DEGs), including KLF4, GAB2, TRAF7, LMO3, SMO and TSLC1 (1316). Additionally, the prognostic capabilities of CKS2, PTTG1 and the leptin receptor have also been indicated by mixed transcriptome analyses (17,18). However, research has mainly focused on identifying candidate genes that may be potential novel biomarkers for meningioma, while the possible intrinsic links among DEGs have not been extensively investigated. Studies aimed at identifying the key pathways and characteristics of the biology involved in this tumor remain limited (11,14,17,18).

Traditional biology research can reveal molecular mechanisms based on the variation and function of an individual gene, mRNA or protein; however, it only describes the biological phenomenon of a disease from a partial viewpoint, rather than describing it in the context of the entire system. Bioinformatics analysis is a powerful tool that provides a novel platform to study the characteristics of biology at a more holistic perspective and elaborate the association of different functional elements (7,15,18).

In the present study, bioinformatics analysis was conducted to determine several potential biomarkers of meningioma (namely JUN, PIK3R1, FOS, AGT and MYC), as well as to identify relevant pathways (including the AGE-RAGE signaling pathway in diabetic complications, PI3K-Akt signaling pathway, ECM-receptor interaction and cell adhesion among others), which are potentially involved in the onset and progression of meningioma. Furthermore, clinical evidence exists to verify the capability of these aforementioned biomarkers and pathways in the prediction of meningioma recurrence and progression. In conclusion, the findings of the present study provide further insight into the pathogenesis of meningiomas and provide potential therapeutic targets for further studies.

Materials and methods

Source of data

Initially, the microarray expression profile of the GSE43290 data set was downloaded from the Gene Expression Omnibus (GEO) database (19). The GSE43290 data set, which includes 47 meningioma samples and 4 normal meningeal samples, was submitted by Tabernero et al (20). The platform of these microarray data, GPL96 [HG-U133A] Affymetrix Human Genome U133A Array, was also downloaded from the GEO database. Using the affy package in R software (version 3.25; www.r-project.org) (21), the obtained raw data were preprocessed, which involved background correction, quartile normalization and probe summarization.

Extraction of differentially expressed genes (DEGs)

A Student's t-test in the Limma package in R software (22) was performed to identify the DEGs between the meningioma and normal meningeal (control) samples. All genes that met the following criteria were selected as DEGs: P-value of <0.05 and |log2(fold change)| of >1. A heat map of the extracted DEGs was then created through the gplots package in R, in order to visualize the expression values of genes in the different samples.

Functional enrichment analysis of DEGs

Following extraction of the DEGs, Gene Ontology (GO) and Kyoto Encyclopedia Genes and Genomes (KEGG) pathway enrichment analyses were conducted. GO analysis is a common bioinformatics method for identifying characteristic biological attributes in large-scale genomic and transcriptomic data (23). KEGG is a database for the systematic analysis of genetic functions that links genomic information with higher order functional information (24). In the present study, the GO analysis was conducted via the Database for Annotation, Visualization and Integrated Discovery (DAVID; https://david.ncifcrf.gov), a web-based tool for systematic functional analysis (25). The GO categories selected included ‘biological process’, ‘molecular function’ and ‘cellular component’. The KEGG pathway analysis of the DEGs was conducted through the ClusterProfiler package in R software. A P-value of <0.05 was selected as the cut-off criterion.

Integration of protein-protein interaction (PPI) network and module analysis

PPI network analysis is a method for identifying the associations among various proteins. To acquire further insights into the molecular mechanisms of meningioma, the list of DEGs was entered into the Search Tool for the Retrieval of Interacting Genes (STRING) database, which is an online database designed to evaluate PPI information (26). Using this tool, gene-gene interactions with a combined score of >0.9 were selected to construct the PPI network. Cytoscape software (version 3.4.0) was then used to visualize the obtained PPI network (27).

All genes with a connectivity degree (defined as the number of other genes that directly interact with that particular gene) of >20 were selected as hub genes in the network. The core genes were the most likely to be involved in meningioma and to be potential biomarkers of tumor development and progression. In addition, significant modules of the PPI network were identified using the Molecular Complex Detection (MCODE) Cytoscape plug-in. An MCODE score (indicating the density of nodes) of >10 and node number of >10 were selected as the significance threshold criteria. Next, KEGG pathway enrichment analysis of the DEGs in these modules was performed using DAVID aiming to evaluate the genetic functions at the molecular level. A P-value of P<0.05 was selected as the cut-off criterion for identifying the significant pathways associated with these modules.

Results

DEGs in meningioma vs. normal meningeal tissues

According to the t-test analysis of the DEGs in the 47 tumor samples compared with the 4 normal meningeal samples, a total of 1,683 DEGs were identified, including 66 upregulated and 1,617 downregulated genes. The heat map of DEG expression is shown in Fig. 1.

Enriched GO terms and KEGG pathways of the identified DEGs

In the present study, a total of 649 enriched GO terms and 34 KEGG pathways were identified. The top 30 enriched GO terms of the DEGs according to the P-value threshold (P<0.05) are shown in Table I. The downregulated genes were significantly associated with ‘protein binding’, ‘cytoplasm’, ‘extracellular matrix (ECM) organization’ and ‘cell adhesion’, whereas there were no GO terms that were significantly enriched among the upregulated DEGs. The enriched KEGG pathways of the DEGs are shown in Table II. A number of the enriched KEGG pathways were directly associated with cancer, including the ‘pathways in cancer’ and ‘small cell lung cancer’ pathways. Furthermore, there was enrichment of certain other pathways that are potentially involved in the development and progression of meningiomas via various biological processes, including the ‘AGE-RAGE signaling pathway in diabetic complications’, ‘PI3K-Akt signaling pathway’, ‘ECM-receptor interaction’ and ‘cell adhesion molecules’.

Table I.

GO analysis of differentially expressed genes associated with meningioma.

Table I.

GO analysis of differentially expressed genes associated with meningioma.

CategoryTermCountP-value
GOTERM_MF_DIRECTProtein binding931 5.26×10−15
GOTERM_CC_DIRECTCytoplasm5871.36×10–13
GOTERM_BP_DIRECTExtracellular matrix organization53 1.22×10−12
GOTERM_CC_DIRECTCytosol3972.33×10–12
GOTERM_BP_DIRECTCell adhesion91 2.85×10−12
GOTERM_CC_DIRECTExtracellular exosome3448.41×10–12
GOTERM_CC_DIRECTExtracellular matrix61 5.24×10−10
GOTERM_CC_DIRECTFocal adhesion739.24×10–10
GOTERM_CC_DIRECTZ disc34 1.06×10−9
GOTERM_BP_DIRECTAngiogenesis512.10×10-9
GOTERM_CC_DIRECTExtracellular space181 2.87×10−9
GOTERM_BP_DIRECTSignal transduction1665.42×10-9
GOTERM_BP_DIRECTPositive regulation of transcription from RNA polymerase II promoter140 1.15×10−7
GOTERM_CC_DIRECTExtracellular region2011.16×10-7
GOTERM_MF_DIRECTTranscription factor binding54 2.02×10−7
GOTERM_CC_DIRECTStress fiber193.71×10-7
GOTERM_BP_DIRECTPositive regulation of angiogenesis30 3.72×10−7
GOTERM_MF_DIRECTIdentical protein binding1093.97×10-7
GOTERM_CC_DIRECTIntegral component of plasma membrane178 4.34×10−7
GOTERM_CC_DIRECTCell surface836.11×10-7
GOTERM_BP_DIRECTType I interferon signaling pathway21 6.20×10−7
GOTERM_BP_DIRECTNegative regulation of cell proliferation686.72×10-7
GOTERM_BP_DIRECTImmune response71 7.37×10−7
GOTERM_BP_DIRECTResponse to hypoxia387.54×10-7
GOTERM_CC_DIRECTMyelin sheath34 7.94×10−7
GOTERM_CC_DIRECTMembrane raft411.24×10-6
GOTERM_CC_DIRECTNeuron projection45 1.34×10−6
GOTERM_CC_DIRECTActin filament201.73×10-6
GOTERM_BP_DIRECTPositive regulation of apoptotic process54 2.63×10−6
GOTERM_CC_DIRECTProteinaceous extracellular matrix483.10×10-6

[i] GO, Gene ontology; MF, molecular function; CC, cellular component; BP, biological process.

Table II.

Enriched Kyoto Encyclopedia of Genes and Genomes pathways of differentially expressed genes associated with meningioma.

Table II.

Enriched Kyoto Encyclopedia of Genes and Genomes pathways of differentially expressed genes associated with meningioma.

Pathway IDDescriptionGene countP-value
hsa04933AGE-RAGE signaling pathway in diabetic complications32 7.86×10−9
hsa04151PI3K-Akt signaling pathway703.98×10-8
hsa04668TNF signaling pathway32 7.73×10−8
hsa04512ECM-receptor interaction261.98×10-7
hsa04510Focal adhesion46 3.84×10−7
hsa05410Hypertrophic cardiomyopathy231.28×10-5
hsa04066HIF-1 signaling pathway26 2.21×10−5
hsa04210Apoptosis322.36×10-5
hsa05146Amoebiasis25 2.62×10−5
hsa05414Dilated cardiomyopathy235.30×10-5
hsa05200Pathways in cancer67 9.01×10−5
hsa05144Malaria151.21×10-4
hsa05222Small cell lung cancer21 2.22×10−4
hsa05134Legionellosis154.93×10-4
hsa05031Amphetamine addiction17 6.46×10−4
hsa04657IL-17 signaling pathway216.90×10-4
hsa05161Hepatitis B29 7.24×10−4
hsa04978Mineral absorption148.68×10-4
hsa04068FoxO signaling pathway27 8.68×10−4
hsa04010MAPK signaling pathway449.13×10-4
hsa04064NF-κB signaling pathway21 9.27×10−4
hsa04060Cytokine-cytokine receptor interaction469.30×10-4
hsa05416Viral myocarditis15 1.10×10−3
hsa05412Arrhythmogenic right ventricular cardiomyopathy171.29×10-3
hsa05202Transcriptional misregulation in cancer33 1.39×10−3
hsa04514Cell adhesion molecules281.40×10-3
hsa05166HTLV–I infection43 2.10×10−3
hsa04261Adrenergic signaling in cardiomyocytes282.14×10-3
hsa04022cGMP-PKG signaling pathway30 3.39×10−3
hsa04145Phagosome283.51×10-3
hsa04610Complement and coagulation cascades17 3.71×10−3
hsa04621NOD-like receptor signaling pathway304.06×10-3
hsa05162Measles25 4.86×10−3
hsa04921Oxytocin signaling pathway285.09×10-3
Module screening from the PPI network

Based on the STRING data, a PPI network of 807 nodes and 2,598 edges was obtained. Nodes with a connectivity degree of >20 were determined as hub genes (Table III). Among them, the top five genes according to their connectivity degree were JUN, PIKR1, FOS, AGT and MYC. In addition, according to the connectivity degree of nodes in modules. The top 4 modules with MCODE score of >10 and node number of >10 were obtained (Fig. 2). Functional annotation results revealed that the genes in modules 1, 2 and 4 were mainly associated with the ‘chemokine signaling pathway’, ‘cytokine-cytokine receptor interaction’, ‘allograft rejection’, and ‘complement and coagulation cascades’, while there were no enriched pathways associated with the DEGs in module 3 (Table IV).

Table III.

Hub genes and their corresponding degree.

Table III.

Hub genes and their corresponding degree.

Gene symbolDegree
JUN79
PIK3R156
FOS53
AGT53
MYC50
STAT347
LPAR147
IL844
HSP90AA141
CXCL1241
NFKB141
RPS27A40
GNAI139
PPBP37
CXCR435
HIF1A33
NPY32
S1PR132
CCL531
SST30
IL630
EDN130
EGR128
STAT128
IRF128
CCR728
CXCL228
SSTR227
CCL1927
RGS127
RGS427
CXCL927
CXCL127
ADRA2A27
HTR1B27
HTR1D27
CXCL327
C5AR127
MTNR1B27
APLNR27
P2RY1427
HCAR327
ICAM125
CDKN1A24
CCND123
PTEN23
NOS323
ACTN123
IRF723
KALRN23
IRF922
HLA-A22
YWHAE22
SIRT121
CDH121
GNAQ21
ISG1520

Table IV.

Enriched Kyoto Encyclopedia of Genes and Genomes pathways of four modules.

Table IV.

Enriched Kyoto Encyclopedia of Genes and Genomes pathways of four modules.

Pathway termP-valueNodes
Module 1
  Chemokine signaling pathway 1.14×10−10CXCL1, CCR7, PPBP, IL8, GNAI1, CXCR4, CXCL3, CXCL2, CXCL9, CCL19, CCL5, CXCL12
  Cytokine-cytokine receptor interaction7.02×10-8CXCL1, CCR7, PPBP, IL8, CXCR4, CXCL3, CXCL2, CXCL9, CCL19, CCL5, CXCL12
  Neuroactive ligand-receptor interaction 7.93×10−7APLNR, HTR1B, SSTR2, C5AR1, S1PR1, P2RY14, ADRA2A, MTNR1B, LPAR1, HTR1D
Module 2
  Allograft rejection0.0418HLA-A, HLA-C
  Graft-versus-host disease0.0452HLA-A, HLA-C
  Type I diabetes mellitus0.0486HLA-A, HLA-C
Module 3
  No record
Module 4
  Complement and coagulation cascades0.0012VWF, A2M, F13A1, SERPINE1
  Calcium signaling pathway0.0018AGTR1, EDNRB, GNAQ, PTGFR, HTR2A
  Renal cell carcinoma0.0198VEGFC, TGFB3, PIK3R1

Discussion

Although previous studies have proposed numerous potential biomarkers associated with the progression and recurrence of meningiomas, the knowledge regarding the molecular mechanisms of meningioma remains relatively limited (13,1618). In the present study, a comprehensive analysis of the gene expression profiles of meningiomas and normal meninges was conducted using a combined bioinformatics approach. A total of 1,683 DEGs (66 upregulated and 1,617 downregulated) were identified. Functional enrichment analysis revealed that these DEGs were mainly involved in ECM organization, cell adhesion, angiogenesis and signal transduction. By constructing a PPI network, a number of hub genes were identified as potential prognostic biomarkers for meningioma.

The gene expression data of 47 meningioma samples and 4 normal controls included in the present study were downloaded from the GEO database with the accession number GSE43290. The 47 tumor samples were composed of 18 diploid tumors, 12 tumors with monosomy 22/del (22q) alone, 4 tumors with del (1p36) alone, and 13 with complex karyotypes associated with del (1p36) and/or del (14q), which are the most frequently altered cytogenetic subgroups of meningiomas in clinical practice (5,12).

The approach used in the current study identified 1,683 DEGs, including 1,617 downregulated and 66 upregulated genes, in meningioma samples as compared with those in normal meninges. These results indicated that gene expression in meningiomas was generally downregulated, which may be attributed to the loss of chromosomal material in meningioma. In addition, GO analysis revealed that the enriched ontological categories among the DEGs mainly included ECM organization, cell adhesion, angiogenesis, signal transduction and negative regulation of cell proliferation. Previous studies have revealed that matrix metalloproteinases (MMPs), which are mediators of invasion and angiogenesis, may serve important roles in the invasion and recurrence of meningioma (28,29). Indeed, cumulative evidence has demonstrated that the contribution of MMPs to tumor progression may be associated with the regulation of cell adhesion, the control of apoptosis via the release of factors associated with cell death or survival, and the proteolysis of the ECM (28,30,31). Previous studies have demonstrated that the aforementioned GO terms are potentially important events in meningioma development and tumor progression. Furthermore, the KEGG pathway analysis results in the present study revealed that ‘ECM-receptor interaction’, ‘apoptosis’ and ‘cell adhesion molecules’ were among the significantly enriched pathways associated with the DEGs. These findings were consistent with those of a study by Keller et al (32), which also suggested that ‘ECM-receptor interaction’ and ‘cell adhesion molecules’ were significantly dysregulated pathways in meningioma. Therefore, monitoring these biological processes and pathways may aid in the prediction of meningioma development and progression. Furthermore, 31 other enriched pathways were identified in the current study, including ‘AGE-RAGE signaling pathway in diabetic complications’, ‘PI3K-Akt signaling pathway’, ‘TNF signaling pathway’ and ‘focal adhesion’. The PI3K-Akt signaling pathway is an intracellular signaling pathway that is important in regulating the cell cycle progression, cell death and cell growth (33). Alterations in this pathway are frequently identified as being involved in the development of various types of cancer (34,35).

The top five hub genes identified from a PPI network constructed from the DEGs in the present study were JUN, PIK3R1, FOS, AGT and MYC. Among these hub genes, JUN, a protein-coding gene, exhibited the highest degree of connectivity. JUN is an important component of activator protein 1 (AP-1), a transcription factor that recognizes the specific DNA sequence TGAC/GTCA. This gene modulates numerous biological functions involved in the regulation of cell proliferation, apoptosis and transformation (36). The aberrant expression of JUN has been reported in various types of cancer, including glioblastoma and hepatocellular carcinoma (37,38). Furthermore, FOS is a member of the Fos family that encodes leucine zipper proteins that form heterodimers with the JUN family, resulting in the formation of AP-1 (39). Thus, this gene also serves important roles in cell proliferation, differentiation and transformation (40). Significant associations between FOS and various tumors have also been identified in previous studies (41,42).

PIK3R1, another hub gene identified in the present study, is a critical mediator of insulin sensitivity, and mutation of this gene is associated with insulin resistance, which is an important mechanism involved in human obesity (43,44). McCurdy et al (45) reported that, in diet-induced obese mice, attenuated PIK3R1 expression was able to prevent insulin resistance. Recently, a large case-control study further suggested that obesity was positively associated with a risk of meningioma (46).

The AGT gene, also identified in the current study, is a member of the renin-angiotensin system-associated gene family, which is physiologically important for blood pressure regulation and may be involved in the pathogenesis of hypertension (47). Accumulating evidence has demonstrated that increased blood pressure is an independent and additive risk factor for the development of brain tumors, particularly meningiomas (46).

Another hub gene, MYC, is located on chromosome 8 and has been closely correlated with cell growth, apoptosis and cellular transformation (48). Mutation, overexpression, rearrangement and translocation of this gene have been detected in a variety of tumors, including Burkitt's lymphoma, medulloblastoma and hepatocellular carcinoma among others (4951).

In the present study, module analysis of the PPI network revealed that the development of meningioma was possibly associated with the chemokine signaling pathway, cytokine-cytokine receptor interaction, allograft rejection, and complement and coagulation cascades. This is consistent with the observations of the study by Keller et al (32), which analyzed the expression profiles of 24 meningiomas and identified ‘cytokine-cytokine receptor interaction’ and ‘complement pathway and coagulation cascades’ as two of the main pathways enriched among the downregulated genes.

In conclusion, by applying a comprehensive bioinformatics analysis of DEGs, the present study identified several hub genes, including JUN, PIK3R1, FOS, AGT and MYC, that may be functionally relevant to the pathogenesis of meningioma. The functional analysis results also revealed a number of potentially significant pathways that may participate in meningioma development and progression, including ‘AGE-RAGE signaling pathway in diabetic complications’, ‘PI3K-Akt signaling pathway’, ‘ECM-receptor interaction’ and ‘cell adhesion molecules’. These results provided further insight into the underlying molecular mechanisms of meningioma. Further experimental studies are required to confirm these observations and to determine their potential as molecular targets in the development of novel therapeutic approaches for meningioma.

Acknowledgements

Not applicable.

Funding

The present study was supported by a grant from the Shanghai Jiao Tong University Medicine and Engineering Cross Fund (grant no. YG 2015MS25).

Availability of data and materials

The datasets analyzed during the current study (GSE43290) were downloaded from a public dataset webset from the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE43290).

Authors' contributions

JD analyzed and interpreted the microarray data regarding meningomas. YM and SC renalyzed the data and confirmed the results' authenticity. NL and JC designed this bioinformatic study and wrote the manuscript. YW was responsible for making tables, drawing the fgures, and helped JD to interprete the findings from the study. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References

1 

Ostrom QT, Blank PMD, Kruchko C, Petersen CM, Liao P, Finlay JL, Stearns DS, Wolff JE, Wolinsky Y, Letterio JJ and Barnholtz-Sloan JS: Alex's lemonade stand foundation infant and childhood primary brain and central nervous system tumors diagnosed in the united states in 2007–2011. Neuro Oncol. 16 Suppl 10:x1–x36. 2015. View Article : Google Scholar : PubMed/NCBI

2 

Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, Scheithauer BW and Kleihues P: The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol. 114:97–109. 2007. View Article : Google Scholar : PubMed/NCBI

3 

Yang SY, Park CK, Park SH, Kim DG, Chung YS and Jung HW: Atypical and anaplastic meningiomas: Prognostic implications of clinicopathological features. J Neurol Neurosurg Psychiatry. 79:574–580. 2008. View Article : Google Scholar : PubMed/NCBI

4 

Riemenschneider MJ, Perry A and Reifenberger G: Histological classification and molecular genetics of meningiomas. Lancet Neurol. 5:1045–1054. 2006. View Article : Google Scholar : PubMed/NCBI

5 

Maillo A, Orfao A, Espinosa AB, Sayagués JM, Merino M, Sousa P, Lara M and Tabernero MD: Early recurrences in histologically benign/grade I meningiomas are associated with large tumors and coexistence of monosomy 14 and del(1p36) in the ancestral tumor cell clone. Neuro Oncol. 9:438–446. 2007. View Article : Google Scholar : PubMed/NCBI

6 

Perry A, Scheithauer BW, Stafford SL, Lohse CM and Wollan PC: ‘Malignancy‘ in meningiomas: A clinicopathologic study of 116 patients, with grading implications. Cancer. 85:2046–2056. 1999. View Article : Google Scholar : PubMed/NCBI

7 

Mawrin C and Perry A: Pathological classification and molecular genetics of meningiomas. J Neurooncol. 99:379–391. 2010. View Article : Google Scholar : PubMed/NCBI

8 

Lomas J, Bello MJ, Arjona D, Alonso ME, Martinez-Glez V, Lopez-Marin I, Amiñoso C, de Campos JM, Isla A, Vaquero J and Rey JA: Genetic and epigenetic alteration of the NF2 gene in sporadic meningiomas. Genes Chromosomes Cancer. 42:314–319. 2005. View Article : Google Scholar : PubMed/NCBI

9 

Harada T, Irving RM, Xuereb JH, Barton DE, Hardy DG, Moffat DA and Maher ER: Molecular genetic investigation of the neurofibromatosis type 2 tumor suppressor gene in sporadic meningioma. J Neurosurg. 84:847–851. 1996. View Article : Google Scholar : PubMed/NCBI

10 

Ng HK, Lau KM, Tse JY, Lo KW, Wong JH, Poon WS and Huang DP: Combined molecular genetic studies of chromosome 22q and the neurofibromatosis type 2 gene in central nervous system tumors. Neurosurgery. 37:764–773. 1995. View Article : Google Scholar : PubMed/NCBI

11 

Choy W, Kim W, Nagasawa D, Stramotas S, Yew A, Gopen Q, Parsa AT and Yang I: The molecular genetics and tumor pathogenesis of meningiomas and the future directions of meningioma treatments. Neurosurg Focus. 30:E62011. View Article : Google Scholar : PubMed/NCBI

12 

Lamszus K, Kluwe L, Matschke J, Meissner H, Laas R and Westphal M: Allelic losses at 1p, 9q, 10q, 14q, and 22q in the progression of aggressive meningiomas and undifferentiated meningeal sarcomas. Cancer Genet Cytogenet. 110:103–110. 1999. View Article : Google Scholar : PubMed/NCBI

13 

Clark VE, Ersonomay EZ, Serin A, Yin J, Cotney J, Ozduman K, Avşar T, Li J, Murray PB, Henegariu O, et al: Genomic analysis of non-NF2 meningiomas reveals mutations in TRAF7, KLF4, AKT1, and SMO. Science. 339:1077–1080. 2013. View Article : Google Scholar : PubMed/NCBI

14 

Chang X, Shi L, Gao F, Russin J, Zeng L, He S, Chen TC, Giannotta SL, Weisenberger DJ, Zada G, et al: Genomic and transcriptome analysis revealing an oncogenic functional module in meningiomas. Neurosurg Focus. 35:E32013. View Article : Google Scholar : PubMed/NCBI

15 

Serna E, Morales JM, Mata M, Gonzalez-Darder J, Miguel San T, Gil-Benso R, Lopez-Gines C, Cerda-Nicolas M and Monleon D: Gene expression profiles of metabolic aggressiveness and tumor recurrence in benign meningioma. PLoS One. 8:e672912013. View Article : Google Scholar : PubMed/NCBI

16 

Surace EI, Lusis E, Murakami Y, Scheithauer BW, Perry A and Gutmann DH: Loss of tumor suppressor in lung cancer-1 (TSLC1) expression in meningioma correlates with increased malignancy grade and reduced patient survival. J Neuropathol Exp Neurol. 63:1015–1027. 2004. View Article : Google Scholar : PubMed/NCBI

17 

Schmidt M, Mock A, Jungk C, Sahm F, Ull AT, Warta R, Lamszus K, Gousias K, Ketter R, Roesch S, et al: Transcriptomic analysis of aggressive meningiomas identifies PTTG1 and LEPR as prognostic biomarkers independent of WHO grade. Oncotarget. 7:14551–14568. 2016. View Article : Google Scholar : PubMed/NCBI

18 

Francesca M, Orzan FN, Eoli M, Farinotti M, Maderna E, Pisati F, Bianchessi D, Valletta L, Lodrini S, Galli G, et al: DNA microarray analysis identifies CKS2 and LEPR as potential markers of meningioma recurrence. Oncologist. 16:1440–1450. 2011. View Article : Google Scholar : PubMed/NCBI

19 

Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, et al: NCBI GEO: Archive for functional genomics data sets-update. Nucleic Acids Res. 41:(Database Issue). D991–D995. 2013. View Article : Google Scholar : PubMed/NCBI

20 

Tabernero MD, Maillo A, Gilbellosta CJ, Castrillo A, Sousa P, Merino M and Orfao A: Gene expression profiles of meningiomas are associated with tumor cytogenetics and patient outcome. Brain Pathol. 19:409–420. 2009. View Article : Google Scholar : PubMed/NCBI

21 

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

22 

Smyth GK: Limma: Linear models for microarray data. In: Bioinformatics and Computational Biology Solutions Using R and BioconductorStatistics for Biology and Health. Gentleman R, Carey V, Huber W, Irizarry RA and Dudoit S: Springer; New York, NY: pp. 397–420. 2005, View Article : Google Scholar

23 

Gene Ontology Consortium: The gene ontology project in 2008. Nucleic Acids Res. 36:(Database Issue). D440–D444. 2008. View Article : Google Scholar : PubMed/NCBI

24 

Ogata H, Goto S, Sato K, Fujibuchi W, Bono H and Kanehisa M: KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 27:29–34. 1999. View Article : Google Scholar : PubMed/NCBI

25 

Huang da W, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocol. 4:44–57. 2009. View Article : Google Scholar

26 

Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, Lin J, Minguez P, Bork P, von Mering C and Jensen LJ: STRING v9.1: Protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41:(Database Issue). D808–D815. 2013. View Article : Google Scholar : PubMed/NCBI

27 

Smoot ME, Ono K, Ruscheinski J, Wang PL and Ideker T: Cytoscape 2.8: New features for data integration and network visualization. Bioinformatics. 27:431–432. 2011. View Article : Google Scholar : PubMed/NCBI

28 

Rooprai HK, Martin AJ, King A, Appadu UD, Jones H, Selway RP, Gullan RW and Pilkington GJ: Comparative gene expression profiling of ADAMs, MMPs, TIMPs, EMMPRIN, EGF-R and VEGFA in low grade meningioma. Int J Oncol. 49:2309–2318. 2016. View Article : Google Scholar : PubMed/NCBI

29 

Kirches E, Grunewald J, Von-Bossanyi P, Szibor R, Plate I, Krüger S, Warich-Kirches M and Dietzmann K: Expression of matrix metalloproteinases in a series of 12 meningiomas. Clin Neuropathol. 20:26–30. 2001.PubMed/NCBI

30 

Roy R, Zhang B and Moses MA: Making the cut: Protease-mediated regulation of angiogenesis. Exp Cell Res. 312:608–622. 2006. View Article : Google Scholar : PubMed/NCBI

31 

Bourboulia D and Stetler-Stevenson WG: Matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs): Positive and negative regulators in tumor cell adhesion. Semin Cancer Biol. 20:161–168. 2010. View Article : Google Scholar : PubMed/NCBI

32 

Keller A, Ludwig N, Backes C, Romeike BF, Comtesse N, Henn W, Steudel WI, Mawrin C, Lenhof HP and Meese E: Genome wide expression profiling identifies specific deregulated pathways in meningioma. Int J Cancer. 124:346–351. 2009. View Article : Google Scholar : PubMed/NCBI

33 

Vara Fresno JA, Casado E, de Castro J, Cejas P, Belda-Iniesta C and González-Barón M: PI3K/Akt signalling pathway and cancer. Cancer Treat Rev. 30:193–204. 2004. View Article : Google Scholar : PubMed/NCBI

34 

Ma YY, Wei SJ, Lin YC, Lung JC, Chang TC, Whang-Peng J, Liu JM, Yang DM, Yang WK and Shen CY: PIK3CA as an oncogene in cervical cancer. Oncogene. 19:2739–2744. 2000. View Article : Google Scholar : PubMed/NCBI

35 

Shayesteh L, Lu Y, Kuo WL, Baldocchi R, Godfrey T, Collins C, Pinkel D, Powell B, Mills GB and Gray JW: PIK3CA is implicated as an oncogene in ovarian cancer. Nat Genet. 21:99–102. 1999. View Article : Google Scholar : PubMed/NCBI

36 

Chen F: JUN (V-Jun sarcoma virus 17 oncogene homolog (avian)). Atlas Genet Cytogenet Oncol Haematol. 7:85–86. 2003.

37 

Wei C, Xiao W, Zhang K, Yin X, Lai J, Liang L and Chen D: Activation of c-Jun predicts a poor response to sorafenib in hepatocellular carcinoma: Preliminary clinical evidence. Sci Rep. 6:229762016. View Article : Google Scholar : PubMed/NCBI

38 

Blau L, Knirsh R, Ben-Dror I, Oren S, Kuphal S, Hau P, Proescholdt M, Bosserhoff AK and Vardimon L: Aberrant expression of c-Jun in glioblastoma by internal ribosome entry site (IRES)-mediated translational activation. Proc Natl Acad Sci USA. 109:E2875–E2884. 2012. View Article : Google Scholar : PubMed/NCBI

39 

Ameyar M, Wisniewska M and Weitzman JB: A role for AP-1 in apoptosis: The case for and against. Biochimie. 85:747–752. 2003. View Article : Google Scholar : PubMed/NCBI

40 

Shaulian E and Karin M: AP-1 in cell proliferation and survival. Oncogene. 20:2390–2400. 2001. View Article : Google Scholar : PubMed/NCBI

41 

Huhe M, Liu S, Zhang Y, Zhang Z and Chen Z: Expression levels of transcription factors c-Fos and c-Jun and transmembrane protein HAb18G/CD147 in urothelial carcinoma of the bladder. Mol Med Rep. 15:2991–3000. 2017. View Article : Google Scholar : PubMed/NCBI

42 

Mahner S, Baasch C, Schwarz J, Hein S, Wölber L, Jänicke F and Milde-Langosch K: C-Fos expression is a molecular predictor of progression and survival in epithelial ovarian carcinoma. Brit J Cancer. 99:1269–1275. 2008. View Article : Google Scholar : PubMed/NCBI

43 

Winnay JN, Solheim MH, Dirice E, Sakaguchi M, Noh HL, Kang HJ, Takahashi H, Chudasama KK, Kim JK, Molven A, et al: PI3-kinase mutation linked to insulin and growth factor resistance in vivo. J Clin Invest. 126:1401–1412. 2016. View Article : Google Scholar : PubMed/NCBI

44 

Thauvin-Robinet C, Auclair M, Duplomb L, Caron-Debarle M, Avila M, St-Onge J, Le Merrer M, Le Luyer B, Héron D, Mathieu-Dramard M, et al: PIK3R1 mutations cause syndromic insulin resistance with lipoatrophy. Am J Hum Genet. 93:141–149. 2013. View Article : Google Scholar : PubMed/NCBI

45 

McCurdy CE, Schenk S, Holliday MJ, Philp A, Houck JA, Patsouris D, MacLean PS, Majka SM, Klemm DJ and Friedman JE: Attenuated Pik3r1 expression prevents insulin resistance and adipose tissue macrophage accumulation in diet-induced obese mice. Diabetes. 61:2495–2505. 2012. View Article : Google Scholar : PubMed/NCBI

46 

Seliger C, Meier CR, Becker C, Jick SS, Proescholdt M, Bogdahn U, Hau P and Leitzmann MF: Metabolic syndrome in relation to risk of meningioma. Oncotarget. 8:2284–2292. 2017. View Article : Google Scholar : PubMed/NCBI

47 

Williamson CR, Khurana S, Nguyen P, Byrne CJ and Tai TC: Comparative analysis of renin-angiotensin system (RAS)-related gene expression between hypertensive and normotensive rats. Med Sci Monit Basic Res. 23:20–24. 2017. View Article : Google Scholar : PubMed/NCBI

48 

Campisi J, Gray HE, Pardee AB, Dean M and Sonenshein GE: Cell-cycle control of c-myc but not c-ras expression is lost following chemical transformation. Cell. 36:241–247. 1984. View Article : Google Scholar : PubMed/NCBI

49 

Fei X, Yuan Y, Xie L, Ran P, Xiang X, Huang Q, Qi G, Guo X, Xiao C and Zheng S: miRNA-320a inhibits tumor proliferation and invasion by targeting c-Myc in human hepatocellular carcinoma. Onco Targets Ther. 10:885–894. 2017. View Article : Google Scholar : PubMed/NCBI

50 

Staal JA, Pei Y and Rood BR: A proteogenomic approach to understanding MYC function in metastatic medulloblastoma tumors. Int J Mol Sci. 17:pii: E1744. 2016. View Article : Google Scholar : PubMed/NCBI

51 

Finver SN, Nishikura K, Finger LR, Haluska FG, Finan J, Nowell PC and Croce CM: Sequence analysis of the MYC oncogene involved in the t(8;14)(q24;q11) chromosome translocation in a human leukemia T-cell line indicates that putative regulatory regions are not altered. Proc Natl Acad Sci USA. 85:3052–3056. 1988. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

June-2018
Volume 15 Issue 6

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
Dai J, Ma Y, Chu S, Le N, Cao J and Wang Y: Identification of key genes and pathways in meningioma by bioinformatics analysis. Oncol Lett 15: 8245-8252, 2018.
APA
Dai, J., Ma, Y., Chu, S., Le, N., Cao, J., & Wang, Y. (2018). Identification of key genes and pathways in meningioma by bioinformatics analysis. Oncology Letters, 15, 8245-8252. https://doi.org/10.3892/ol.2018.8376
MLA
Dai, J., Ma, Y., Chu, S., Le, N., Cao, J., Wang, Y."Identification of key genes and pathways in meningioma by bioinformatics analysis". Oncology Letters 15.6 (2018): 8245-8252.
Chicago
Dai, J., Ma, Y., Chu, S., Le, N., Cao, J., Wang, Y."Identification of key genes and pathways in meningioma by bioinformatics analysis". Oncology Letters 15, no. 6 (2018): 8245-8252. https://doi.org/10.3892/ol.2018.8376