Prediction and Analysis of Key Genes in Glioblastoma Based on Bioinformatics

Long, Hao; Liang, Chaofeng; Zhang, Xian; Fang, Luxiong; Wang, Gang; Qi, Songtao; Huo, Haizhong; Song, Ye

doi:10.1155/2017/7653101

Cited by 32 publications

(23 citation statements)

References 26 publications

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“…FN1 encodes fibronectin, which is involved in cell adhesion and migration. Some studies have found that FN1 is the hub gene in glioma, a result that is consistent with our findings; as such, FN1 is a potential target for diagnosis and therapy 54 , 55 . APP mutations cause Alzheimer’s disease, and over-expression of APP has a link with shortened survival in patients with breast cancer 56 .…”

Section: Discussionsupporting

confidence: 92%

Gene expression meta-analysis in diffuse low-grade glioma and the corresponding histological subtypes

Wang

Jin

Fan

et al. 2017

Sci Rep

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Diffuse low-grade glioma (DLGG) is a well-differentiated, slow-growing tumour with an inherent tendency to progress to high-grade glioma. The potential roles of genetic alterations in DLGG development have not yet been fully delineated. Therefore, the current study performed an integrated gene expression meta-analysis of eight independent, publicly available microarray datasets including 291 DLGGs and 83 non-glioma (NG) samples to identify gene expression signatures associated with DLGG. Using INMEX, 708 differentially expressed genes (DEGs) (385 upregulated and 323 downregulated genes) were identified in DLGG compared to NG. Furthermore, 497 DEGs (222 upregulated and 275 downregulated genes) corresponding to two histological types were identified. Of these, high expression of HIP1R significantly correlated with increased overall survival, whereas high expression of TBXAS1 significantly correlated with decreased overall survival. Additionally, network-based meta-analysis identified FN1 and APP as the key hub genes in DLGG compared with NG. PTPN6 and CUL3 were the key hub genes identified in the astrocytoma relative to the oligodendroglioma. Further immunohistochemical validation revealed that MTHFD2 and SPARC were positively expressed in DLGG, whereas RBP4 was positively expressed in NG. These findings reveal potential molecular biomarkers for diagnosis and therapy in patients with DLGG and provide a rich and novel candidate reservoir for future studies.

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Section: Discussionsupporting

confidence: 92%

Gene expression meta-analysis in diffuse low-grade glioma and the corresponding histological subtypes

Wang

Jin

Fan

et al. 2017

Sci Rep

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“…Two studies have identified the potential role of SV2B in promoting tumor metastasis, with MMP9, COL3A1 and SV2B being identified as important hub genes that are associated with ECM-receptor interaction [31]. It has been proposed that PAK1 and SV2B may be GBM-associated prognostic markers.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics analysis of potential core genes for glioblastoma

et al. 2020

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Background: Glioblastoma (GBM) has a high degree of malignancy, aggressiveness and recurrence rate. However, there are limited options available for the treatment of GBM, and they often result in poor prognosis and unsatisfactory outcomes. Materials and methods: In order to identify potential core genes in GBM that may provide new therapeutic insights, we analyzed three gene chips (GSE2223, GSE4290 and GSE50161) screened from the GEO database. Differentially expressed genes (DEG) from the tissues of GBM and normal brain were screened using GEO2R. To determine the functional annotation and pathway of DEG, Gene Ontology (GO) and KEGG pathway enrichment analysis were conducted using DAVID database. Protein interactions of DEG were visualized using PPI network on Cytoscape software. Next, 10 Hub nodes were screened from the differentially expressed network using MCC algorithm on CytoHubba software and subsequently identified as Hub genes. Finally, the relationship between Hub genes and the prognosis of GBM patients was described using GEPIA2 survival analysis web tool. Results: A total of 37 up-regulated and 187 down-regulated genes were identified through microarray analysis. Amongst the 10 Hub genes selected, SV2B appeared to be the only gene associated with poor prognosis in glioblastoma based on the survival analysis. Conclusion: Our study suggests that high expression of SV2B is associated with poor prognosis in GBM patients. Whether SV2B can be used as a new therapeutic target for GBM requires further validation.

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“…[44] Besides, miR-25 and -32 might serve as positive regulators of p53, underscoring their role in tumorigenesis in glioblastoma. [45] The FN1 gene has been identified as a key gene of GBM [15] and one of the cis-expression quantitative trait locus (eQTL). [46] It's also reported that FN1 is closely related to cell adhesion and invasion in glioblastoma.…”

Section: Survival Analysismentioning

confidence: 99%

“…[9][10][11] Recently, microarray and high-throughput genomic sequencing technology has significantly facilitated the detection of critical genetic or epigenetic alternations in carcinogenesis and the identification of novel promising diagnostic, prognostic biomarkers and therapeutic targets. [12][13][14][15] Over the past years, expanding data and accumulating results from computational analyses furthering the understanding of underlying pathophysiological mechanisms and prognosis of GBM. [16,17] Moreover, in order to overcome the restricted or inconsistent results due to the application of either different technological platforms or a small sample size, integrated bioinformatics strategies have been adopted in cancer research and a large amount of valuable biological information has been uncovered.…”

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confidence: 99%

Identification of Potential Key Genes Associated with the Pathogenesis and Prognosis of Glioblastoma Based on Integrated Bioinformatics Analysis

Tu¹,

Zhou²,

Zhang³

et al. 2020

Preprint

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Background: Despite striking advances in multimodality management, the low survival rate of Glioblastoma (GBM) patients has not been significantly improved and identifying novel diagnostic and prognostic biomarkers is urgently demanded. The present study aimed to identify potential key genes associated with the pathogenesis and prognosis of GBM.Methods: Differentially expressed genes between GBM and normal brain tissue samples were screened by an integrated analysis of multiple gene expression profile datasets. Key genes related to the pathogenesis and prognosis of GBM were identified by employing protein–protein interaction network and Cox proportional hazards model analyses.Results: We identified nine hub genes (TP53, FN1, EGFR, MYC, RRM2, EZH2, FOXM1, CD44 and MMP2) which might be closely associated with the pathogenesis of GBM. A prognostic gene signature consisted of RAB33B, KIAA1199, TEK, EVC, SOD2, CXCR4, hCG_40738, CHD9, GCSH, SUHW1, RPS6KA5, PDCD4, ZG16, KCNG1, DECR1, PPCS, SERPINF1, TMSB10, NAT10, HIC2, PIR and OR2W1 was constructed with a good performance in predicting overall survivals (OS).Conclusions: The findings of present study would provide certain reference for further predicting the diagnostic and prognostic biomarkers to facilitate the molecular targeting therapy of GBM.

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Prediction and Analysis of Key Genes in Glioblastoma Based on Bioinformatics

Cited by 32 publications

References 26 publications

Gene expression meta-analysis in diffuse low-grade glioma and the corresponding histological subtypes

Gene expression meta-analysis in diffuse low-grade glioma and the corresponding histological subtypes

Bioinformatics analysis of potential core genes for glioblastoma

Identification of Potential Key Genes Associated with the Pathogenesis and Prognosis of Glioblastoma Based on Integrated Bioinformatics Analysis

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