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

Glioblastoma stem cells (GSC) co-exhibiting a tumor-initiating capacity and a radio-chemoresistant phenotype, are a compelling cell model for explaining tumor recurrence. We have previously characterized patient-derived, treatment-resistant GSC clones (TRGC) that survived radiochemotherapy. Compared to glucose-dependent, treatment-sensitive GSC clones (TSGC), TRGC exhibited reduced glucose dependence that favor the fatty acid oxidation pathway as their energy source. Using comparative genome-wide transcriptome analysis, a series of defense signatures associated with TRGC survival were identified and verified by siRNA-based gene knockdown experiments that led to loss of cell integrity. In this study, we investigate the prognostic value of defense signatures in glioblastoma (GBM) patients using gene expression analysis with Probeset Analyzer (131 GBM) and The Cancer Genome Atlas (TCGA) data, and protein expression with a tissue microarray (50 GBM), yielding the first TRGC-derived prognostic biomarkers for GBM patients. Ribosomal protein S11 (RPS11), RPS20, individually and together, consistently predicted poor survival of newly diagnosed primary GBM tumors when overexpressed at the RNA or protein level [RPS11: Hazard Ratio (HR) = 11.5, p<0.001; RPS20: HR = 4.5, p = 0.03; RPS11+RPS20: HR = 17.99, p = 0.001]. The prognostic significance of RPS11 and RPS20 was further supported by whole tissue section RPS11 immunostaining (27 GBM; HR = 4.05, p = 0.01) and TCGA gene expression data (578 primary GBM; RPS11: HR = 1.19, p = 0.06; RPS20: HR = 1.25, p = 0.02; RPS11+RPS20: HR = 1.43, p = 0.01). Moreover, tumors that exhibited unmethylated O-6-methylguanine-DNA methyltransferase (MGMT) or wild-type isocitrate dehydrogenase 1 (IDH1) were associated with higher RPS11 expression levels [corr (IDH1, RPS11) = 0.64, p = 0.03); [corr (MGMT, RPS11) = 0.52, p = 0.04]. These data indicate that increased expression of RPS11 and RPS20 predicts shorter patient survival. The study also suggests that TRGC are clinically relevant cells that represent resistant tumorigenic clones from patient tumors and that their properties, at least in part, are reflected in poor-prognosis GBM. The screening of TRGC signatures may represent a novel alternative strategy for identifying new prognostic biomarkers.
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PMID:Ribosomal Proteins RPS11 and RPS20, Two Stress-Response Markers of Glioblastoma Stem Cells, Are Novel Predictors of Poor Prognosis in Glioblastoma Patients. 2650 20

For many years, defects in the ribosome have been associated to cancer. Recently, somatic mutations and deletions affecting ribosomal protein genes were identified in a few leukemias and solid tumor types. However, systematic analysis of all 81 known ribosomal protein genes across cancer types is lacking. We screened mutation and copy number data of respectively 4926 and 7322 samples from 16 cancer types and identified six altered genes (RPL5, RPL11, RPL23A, RPS5, RPS20 and RPSA). RPL5 was located at a significant peak of heterozygous deletion or mutated in 11% of glioblastoma, 28% of melanoma and 34% of breast cancer samples. Moreover, patients with low RPL5 expression displayed worse overall survival in glioblastoma and in one breast cancer cohort. RPL5 knockdown in breast cancer cell lines enhanced G2/M cell cycle progression and accelerated tumor progression in a xenograft mouse model. Interestingly, our data suggest that the tumor suppressor role of RPL5 is not only mediated by its known function as TP53 or c-MYC regulator. In conclusion, RPL5 heterozygous inactivation occurs at high incidence (11-34%) in multiple tumor types, currently representing the most common somatic ribosomal protein defect in cancer, and we demonstrate a tumor suppressor role for RPL5 in breast cancer.
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PMID:The ribosomal protein gene RPL5 is a haploinsufficient tumor suppressor in multiple cancer types. 2814 43

Detection and diagnosis of cancer are especially important for early prevention and effective treatments. Traditional methods of cancer detection are usually time-consuming and expensive. Liquid biopsy, a newly proposed noninvasive detection approach, can promote the accuracy and decrease the cost of detection according to a personalized expression profile. However, few studies have been performed to analyze this type of data, which can promote more effective methods for detection of different cancer subtypes. In this study, we applied some reliable machine learning algorithms to analyze data retrieved from patients who had one of six cancer subtypes (breast cancer, colorectal cancer, glioblastoma, hepatobiliary cancer, lung cancer and pancreatic cancer) as well as healthy persons. Quantitative gene expression profiles were used to encode each sample. Then, they were analyzed by the maximum relevance minimum redundancy method. Two feature lists were obtained in which genes were ranked rigorously. The incremental feature selection method was applied to the mRMR feature list to extract the optimal feature subset, which can be used in the support vector machine algorithm to determine the best performance for the detection of cancer subtypes and healthy controls. The ten-fold cross-validation for the constructed optimal classification model yielded an overall accuracy of 0.751. On the other hand, we extracted the top eighteen features (genes), including TTN, RHOH, RPS20, TRBC2, in another feature list, the MaxRel feature list, and performed a detailed analysis of them. The results indicated that these genes could be important biomarkers for discriminating different cancer subtypes and healthy controls.
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PMID:Identifying and analyzing different cancer subtypes using RNA-seq data of blood platelets. 2915 97