Gene/Protein Disease Symptom Drug Enzyme Compound
Pivot Concepts:   Target Concepts:
Query: UMLS:C0017636 (glioblastoma)
 18,345 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

To determine the prognostic significance of kinesin superfamily gene (KIF) expression in patients with brain cancer, including low-grade glioma (LGG) and glioblastoma (GBM), we comprehensively analyzed KIFs in 515 LGG and 595 GBM patients. Among KIFs, KIF4A, 9, 18A, and 23 showed significant clinical implications in both LGG and GBM. The mRNA and protein expression levels of KIF4A, 9, 18A, and 23 were significantly increased in LGG and GBM compared with those in the normal control groups. The mRNA expression levels of KIF4A, 9, 18A, and 23 in LGG were significantly increased in the high-histologic-grade group compared with those with a low histologic grade. Genomic analysis showed that the percent of mRNA upregulation of KIF4A, 9, 18A, and 23 was higher than that of other gene alterations, including gene amplification, deep deletion, and missense mutation. In addition, LGG patients with KIF4A, 18A, and 23 gene alterations were significantly associated with a poor prognosis. In survival analysis, the group with high expression of KIF4A, 9, 18A, and 23 mRNA was significantly associated with a poor prognosis in both LGG and GBM patients. Gene Set Enrichment Analysis (GSEA) revealed that high mRNA expression of KIF4A, 18A, and 23 in LGG and GBM patients showed significant positive correlations with the cell cycle, E2F targets, G2M checkpoint, Myc target, and mitotic spindle. By contrast, high mRNA expression of KIF9 in both LGG and GBM patients was significantly negatively correlated with the cell cycle, G2M checkpoint, and mitotic spindle pathway. However, it was significantly positively correlated with EMT and angiogenesis. This study has extended our knowledge of KIF4A, 9, 18A, and 23 in LGG and GBM and shed light on their clinical relevance, which should help to improve the treatment and prognosis of LGG and GBM.
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PMID:Integrative analysis of KIF4A, 9, 18A, and 23 and their clinical significance in low-grade glioma and glioblastoma. 3087 92

Glioblastoma is a common malignant tumor in the central nervous system with an extremely poor outcome; understanding the mechanisms of glioblastoma at the molecular level is essential for clinical treatment. In the present study, we used bioinformatics analysis to identify potential biomarkers associated with prognosis in glioblastoma and elucidate the underlying mechanisms. The result revealed that 552 common genes were differentially expressed between glioblastoma and normal tissues based on TCGA, GSE4290, and GSE 50161 datasets. Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and protein-protein interaction (PPI) network were carried out to gain insight into the actions of differentially expressed genes (DEGs). As a result, 20 genes (CALB1, CDC20, CDCA8, CDK1, CEP55, DLGAP5, KIF20A, KIF4A, NDC80, PBK, RRM2, SYN1, SYP, SYT1, TPX2, TTK, VEGFA, BDNF, GNG3, and TOP2A) were found as hub genes via CytoHubba in Cytoscape and functioned mainly by participating in cell cycle and p53 signaling pathway; among them, RRM2 and CEP55 were considered to have relationship with the prognosis of glioblastoma, especially RRM2. High expression of RRM2 was consistent with shorter overall survival time. In conclusion, our study displayed the bioinformatic analysis methods in screening potential oncogenes in glioblastoma and underlying mechanisms. What is more is that we successfully identified RRM2 as a novel biomarker linked with prognosis, which might be expected to be a promising target for the therapy of glioblastoma.
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PMID:Identification of Potential Biomarkers in Glioblastoma through Bioinformatic Analysis and Evaluating Their Prognostic Value. 3111 82