4.6 Article

Identification of genes from ten oncogenic pathways associated with mortality and disease progression in glioblastoma

Journal

FRONTIERS IN ONCOLOGY
Volume 12, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fonc.2022.965638

Keywords

glioblastoma multiforme; oncogenic signaling pathways; The Cancer Genome Atlas; survival; gene

Categories

Funding

  1. NRF/MSIT [2018R1A5A7059549, 2021M3E5D2A01019545]
  2. IITP/MSIT Artifcial Intelligence Graduate School Program for Hanyang University [2020-0-01373]
  3. National Research Foundation of Korea [2021M3E5D2A01019545] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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This study investigated the expression of genes in 10 canonical oncogenic signaling pathways and their relationship to mortality and disease progression in GBM patients. It identified 12 independent genes significantly associated with poor prognosis in GBM and suggested possible mechanisms affecting GBM prognosis.
Glioblastoma multiforme (GBM) is the most malignant brain tumor with an extremely poor prognosis. The Cancer Genome Atlas (TCGA) database has been used to confirm the roles played by 10 canonical oncogenic signaling pathways in various cancers. The purpose of this study was to evaluate the expression of genes in these 10 canonical oncogenic signaling pathways, which are significantly related to mortality and disease progression in GBM patients. Clinicopathological information and mRNA expression data of 525 patients with GBM were obtained from TCGA database. Gene sets related to the 10 oncogenic signaling pathways were investigated via Gene Set Enrichment Analysis. Multivariate Cox regression analysis was performed for all the genes significantly associated with mortality and disease progression for each oncogenic signaling pathway in GBM patients. We found 12 independent genes from the 10 oncogenic signaling pathways that were significantly related to mortality and disease progression in GBM patients. Considering the roles of these 12 significant genes in cancer, we suggest possible mechanisms affecting the prognosis of GBM. We also observed that the expression of 6 of the genes significantly associated with a poor prognosis of GBM, showed negative correlations with CD8+ T-cells in GBM tissue. Using a large-scale open database, we identified 12 genes belonging to 10 well-known oncogenic canonical pathways, which were significantly associated with mortality and disease progression in patients with GBM. We believe that our findings will contribute to a better understanding of the mechanisms underlying the pathophysiology of GBM in the future.

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