2019
DOI: 10.1186/s12967-019-1930-3
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Integrated analysis of 34 microarray datasets reveals CBX3 as a diagnostic and prognostic biomarker in glioblastoma

Abstract: Background Glioblastomas have a high degree of malignancy, high recurrence rate, high mortality rate, and low cure rate. Searching for new markers of glioblastomas is of great significance for improving the diagnosis, prognosis and treatment of glioma. Methods Using the GEO public database, we combined 34 glioma microarray datasets containing 1893 glioma samples and conducted genetic data mining through statistical analysis, bioclustering, and pathway analysis. The resu… Show more

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Cited by 29 publications
(18 citation statements)
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“…32 Knocking down CBX3 can arrest cell cycle in the G2/M cell cycle transition, thereby suppressing the proliferation of cancer cells in pancreatic adenocarcinoma 33 and glioblastoma. 34 Similarly, our results show that CBX3 inhibition affects cell cycle transformation not only in G1/S phase but also in G2/M phase.…”
Section: Discussionsupporting
confidence: 76%
“…32 Knocking down CBX3 can arrest cell cycle in the G2/M cell cycle transition, thereby suppressing the proliferation of cancer cells in pancreatic adenocarcinoma 33 and glioblastoma. 34 Similarly, our results show that CBX3 inhibition affects cell cycle transformation not only in G1/S phase but also in G2/M phase.…”
Section: Discussionsupporting
confidence: 76%
“…In recent years, accumulated evidence suggested that amplification of the EGFR and other genes might play a critical role in the oncogenesis and clinical behavior of GBM [22,26,27]. Despite this, with the exception of a recent study [28], no clear association has been reported in the literature between the GEP and the genetic alterations of individual genes in GBM [29]. In order to investigate the potential association between overexpression of EGFR, CDK4, MDM4, and PDGFRA, subsequent analysis of gene amplification was performed at the DNA level.…”
Section: Discussionmentioning
confidence: 97%
“…Because of the complexity of lung cancer [43], results derived from GEO and TCGA datasets were not always consistent with each other. Also, the difference in treatment protocol and microarray platform in individual study limited the further applications of these findings [44]. For example, CAD, CTPS, and DHODH had no prognostic effect in LUAD dataset but associated with the clinical outcomes in lung cancer patients derived from GEO datasets.…”
Section: Discussionmentioning
confidence: 99%