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)

We examined whole genomic aberrations of biopsied samples from 19 independent glioblastomas by array-based comparative genomic hybridization analysis. The highest frequencies of copy number gains were observed on RFC2 (73.3%), EGFR (63.2%), and FGR, ELN, CDKN1C , FES, TOP2A, and ARSA (57.9% each). The highest frequencies of copy number losses were detected on TBR1 (52.6%), BMI1 (52.6%), EGR2 (47.4%), DMBT1 (47.4%), MTAP (42.1%), and FGFR2 (42.1%). The copy number gains of CDKN1C and INS and the copy number losses of TBR1 were significantly correlated with longer survival of patients. High-level amplifications were identified on EGFR, SAS/CDK4, PDGFRA, MDM2, and ARSA. These genes are assumed to be involved in tumorigenesis or progression of glioblastomas. The first attempts to apply detrended fluctuation analysis to copy number profiles by considering the reading direction as the time axis demonstrated that higher long-term fractal scaling exponents (alpha2) correlated well with longer survival of glioblastoma patients. The present study indicates that array-based comparative genomic hybridization analysis has great potential for assessment of copy number changes and altered chromosomal regions of brain tumors. Furthermore, we show that nonlinear analysis methods of whole genome copy number profiles may provide prognostic information about glioblastoma patients.
 ...
PMID:Detrended fluctuation analysis of genome-wide copy number profiles of glioblastomas using array-based comparative genomic hybridization. 1549 95

Abnormal expression of DNA repair genes is frequently associated with cancerogenesis of many tumors, however, the role DNA repair genes play in the progression of glioblastoma remains unclear. In this study, taking advantage of large scale of RNA-seq data, as well as clinical data, the function and prognosis value of key DNA repair genes in glioblastoma were analyzed by systematically bioinformatic approaches. Clustering was performed to screen potentially abnormal DNA repair genes related to the prognosis of glioblastoma, followed by unsupervised clustering to identify molecular subtypes of glioblastomas. Characteristics and prognosis differences were analyzed among these molecular subtypes, and modular driver genes in molecular subtypes were identified based on changes in expression correlation. Multifactor Cox proportional hazard analysis was used to find the independent prognostic factor. A total of 15 key genes, which were significantly related to prognosis, were identified and four molecular subtypes of disease were obtained through unsupervised clustering, based on these 15 genes. By analyzing the clinical features of these 4 molecular subtypes, Cluster 4 was found to be different from others in terms of age and prognosis level. A total of 5 key DNA repair genes, CDK7, DDB2, RNH1, RFC2 and FAH, were screened to be significantly related to the prognosis of glioblastomas (p = 9.74e-05). In summary, the DNA repair genes which can predict the prognosis of patients with Glioblastoma multiforme (GBM) were identified and validated. The expression level of DNA repair genes shows the potential of predicting the prognosis and therapy design in targeting GBM.
 ...
PMID:Prognostic value of DNA repair genes based on stratification of glioblastomas. 2893 50