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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 / Copyright

    Affiliations: Department of Neurosurgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201900, P.R. China, Department of Emergency, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200082, P.R. China
    Copyright: © Dai et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Pages: 8245-8252
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    Published online on: March 29, 2018
       https://doi.org/10.3892/ol.2018.8376
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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.
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Copy and paste a formatted citation
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
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
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