Abstract
Glioblastoma (GBM) is a malignant brain tumour with poor prognosis. The potential pathogenesis and therapeutic target are still need to be explored. Herein, TCGA expression profile data and clinical information were downloaded, and the WGCNA was conducted. Hub genes which closely related to poor prognosis of GBM were obtained. Further, the relationship between the genes of interest and prognosis of GBM, and immune microenvironment were analysed. Patients from TCGA were divided into high‐ and low‐risk group. WGCNA was applied to the high‐ and low‐risk group and the black module with the lowest preservation was identified which could distinguish the prognosis level of these two groups. The top 10 hub genes which were closely related to poor prognosis of patients were obtained. GO analysis showed the biological process of these genes mainly enriched in: Cell cycle, Progesterone‐mediated oocyte maturation and Oocyte meiosis. CDCA5 and CDCA8 were screened out as the genes of interest. We found that their expression levels were closely related to overall survival. The difference analysis resulted from the TCGA database proved both CDCA5 and CDCA8 were highly expressed in GBM. After transfection of U87‐MG cells with small interfering RNA, it revealed that knockdown of the CDCA5 and CDCA8 could influence the biological behaviours of proliferation, clonogenicity and apoptosis of GBM cells. Then, single‐gene analysis was performed. CDCA5 and CDCA8 both had good correlations with genes that regulate cell cycle in the p53 signalling pathway. Moreover, it revealed that high amplification of CDCA5 was correlated with CD8+ T cells while CDCA8 with CD4+ T cells in GBM. These results might provide new molecular targets and intervention strategy for GBM.
Highlights
Glioblastoma (GBM) is the most common malignant tumor of the central nervous system (CNS) in adults [1, 2]
The results showed that these data sets had not been corrected due to the batch removal effect, and the tree graph and samples were clustered in the correct random order (Figure 1)
Due to the premise of Weighted Gene Co-expression Network Analysis (WGCNA) algorithm needs to assume that gene network is subject to scale-free distribution
Summary
Glioblastoma (GBM) is the most common malignant tumor of the central nervous system (CNS) in adults [1, 2]. It comprises 45.2% of CNS tumors and 54% of all gliomas [3]. The potential heterogeneities and complexities of GBM make it difficult to identify reliable factors for determining effective clinical treatment. It is urgently needed to uncover efficient molecular targets which can clinically significance contribute to the personalized treatment and improve prognosis for GBM patients. Glioblastoma (GBM) is a malignant brain tumor with poor prognosis. The potential pathogenesis and therapeutic target s still needs to be explored
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