Query: gbm signature geo

title: A Novel Assay for Profiling GBM Cancer Model Heterogeneity and Drug Screening
abstract: Accurate patient derived models of cancer are needed for profiling the disease and for testing therapeutics. These models must not only be accurate, but also suitable for high throughput screening and analysis. Here we compare two derivative cancer models, microtumors and spheroids, to the gold standard model of patient derived orthotopic xenografts (PDX) in glioblastoma multiforme (GBM) To compare these models, we constructed a custom NanoString panel of 350 genes relevant to GBM biology. This custom assay includes 16 GBM specific gene signatures including a novel GBM subtyping signature. We profiled 11 GBM PDX with matched orthotopic cells, derived microtumors, and derived spheroids using the custom NanoString assay. In parallel, these derivative models underwent drug sensitivity screening. We found that expression of certain genes were dependent on the cancer model while others were model independent. These model independent genes can be used in profiling tumor specific biology and in gauging therapeutic response. It remains to be seen whether or not cancer model specific genes may be directly or indirectly, through changes to tumor microenvironment, manipulated to improve the concordance of in vitro derivative models with in vivo models yielding better prediction of therapeutic response.

title: Expression Profile Analysis Identifies a Novel Five Gene Signature to Improve Prognosis Prediction of Glioblastoma
abstract: Glioblastoma multiforme (GBM) is the most aggressive primary central nervous system malignant tumor. The median survival of GBM patients is 12 15 months, and the 5 years survival rate is less than 5% More novel molecular biomarkers are still urgently required to elucidate the mechanisms or improve the prognosis of GBM. This study aimed to explore novel biomarkers for GBM prognosis prediction. The gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets of GBM were downloaded. A total of 2241 overlapping differentially expressed genes (DEGs) were identified from TCGA and GSE7696 datasets. By univariate COX regression survival analysis, 292 survival related genes were found among these DEGs (p 0.05) Functional enrichment analysis was performed based on these survival related genes. A five gene signature (PTPRN, RGS14, G6PC3, IGFBP2, and TIMP4) was further selected by multivariable Cox regression analysis and a prognostic model of this five gene signature was constructed. Based on this risk score system, patients in the high risk group had significantly poorer survival results than those in the low risk group. Moreover, with the assistance of GEPIA http:/gepia.cancer pku.cn/ all five genes were found to be differentially expressed in GBM tissues compared with normal brain tissues. Furthermore, the co expression network of the five genes was constructed based on weighted gene co expression network analysis (WGCNA) Finally, this five gene signature was further validated in other datasets. In conclusion, our study identified five novel biomarkers that have potential in the prognosis prediction of GBM.

title: Combined Therapy Sensitivity Index Based on a 13 Gene Signature Predicts Prognosis for IDH Wild type and MGMT Promoter Unmethylated Glioblastoma Patients
abstract: Glioblastoma (GBM) is one of the lethal tumors with poor prognosis. However, prognostic prediction approaches need to be further explored. Therefore, we developed an evaluation system that could be used for prognostic prediction of GBM patients. Published mRNA expression datasets from The Cancer Genome Atlas (TCGA) Gene Expression Omnibus (GEO) and Chinese Glioma Genome Atlas (CGGA) were analyzed. Quantitative Realtime PCR of signature genes and molecular aberrations of 178 Xiangya GBM patients were used for confirmation. Gene set enrichment analysis (GSEA) was performed for functional annotation. As a result, we established a 13 gene signature which named Combined Therapy Sensitivity Index (CTSI) Based on a cutoff point, we divided patients into high risk group and low risk group. Based on Kaplan Meier analysis and multivariate Cox regression analysis, we found that patients in the high risk group had a shorter overall survival time than patients in the low risk group (p<0.001 in TCGA and CGGA datasets, p=0.047 in GSE4271 dataset, p=0.008 in Xiangya GBM cohort, HR: 1.65 3.42) By comparing the status of IDH mutation, TERT promoter mutation (TERTp mut) and MGMT promoter methylation, CTSI was predictable in IDH wild type (IDH wt)/MGMT promoter unmethylated (MGMTp unmeth) patients (p=0.037 in IDH wt/TERTp mut/MGMTp unmeth subgroup, HR: 1.98; p=0.032 in IDH wt/TERTp wt/MGMTp unmeth subgroup, HR: 2.09) Based on GESA, the Gene Ontology (GO) gene sets were enriched differently between CTSI high risk and low risk groups. Our results showed CTSI risk score can predict the prognosis of IDH wt/MGMTp unmeth GBM patients. Based on CTSI, combined with the status of IDH mutation, TERT promoter mutation and MGMT promoter methylation, a stepwise prognosis evaluation system which can provide precise prognosis prediction for GBM patients was established.

title: A four gene signature derived risk score for glioblastoma: prospects for prognostic and response predictive analyses
abstract: (GBM) is the most common primary malignant brain tumor regulated by numerous genes, with poor survival outcomes and unsatisfactory response to therapy. Therefore, a robust, multi gene signature derived model is required to predict the prognosis and treatment response in GBM.  Gene expression data of GBM from TCGA and GEO datasets were used to identify differentially expressed genes (DEGs) through DESeq2 or LIMMA methods. The DEGs were then overlapped and used for survival analysis by univariate and multivariate COX regression. Based on the gene signature of multiple survival associated DEGs, a risk score model was established, and its prognostic and predictive role was estimated through Kaplan Meier analysis and log rank test. Gene set enrichment analysis (GSEA) was conducted to explore high risk score associated pathways. Western blot was used for protein detection.  Four survival associated DEGs of GBM were identified: OSMR, HOXC10, SCARA3, and SLC39A10. The four gene signature derived risk score was higher in GBM than in normal brain tissues. GBM patients with a high risk score had poor survival outcomes. The high risk group treated with temozolomide chemotherapy or radiotherapy survived for a shorter duration than the low risk group. GSEA showed that the high risk score was enriched with pathways such as vasculature development and cell adhesion. Western blot confirmed that the proteins of these four genes were differentially expressed in GBM cells.  The four gene signature derived risk score functions well in predicting the prognosis and treatment response in GBM and will be useful for guiding therapeutic strategies for GBM patients.

title: A Potential Prognostic Gene Signature for Predicting Survival for Glioblastoma Patients
abstract: This study aimed to screen prognostic gene signature of glioblastoma (GBM) to construct prognostic model.  Based on the GBM information in the Cancer Genome Atlas (TCGA, training set) prognostic genes (Set X) were screened by Cox regression. Then, the optimized prognostic gene signature (Set Y) was further screened by the Cox Proportional Hazards (Cox PH) Next, two prognostic models were constructed: model A was based on the Set Y; model B was based on part of the Set X. The samples were divided into low and high risk groups according to the median prognosis index (PI) GBM datasets in Gene Expression Ominous (GEO, GSE13041) and Chinese Glioma Genome Atlas (CGGA) were used as the testing datasets to confirm the prognostic models constructed based on TCGA.  We identified that the prognostic 14 gene signature was significantly associated with the overall survival (OS) in the TCGA. In model A, patients in high and low risk groups showed the significantly different OS (P 7.47 x 10 9, area under curve (AUC) 0.995) and the prognostic ability were also confirmed in testing sets (P=0.0098 and 0.037) The model B in training set was significant but failed in testing sets.  The prognostic model which was constructed based on the prognostic 14 gene signature presented a high predictive ability for GBM. The 14 gene signature may have clinical implications in the subclassification of GBM.

title: Identification of a multidimensional transcriptome signature for survival prediction of postoperative glioblastoma multiforme patients
abstract: BackgroundGlioblastoma multiform (GBM) is a devastating brain tumor with maximum surgical resection, radiotherapy plus concomitant and adjuvant temozolomide (TMZ) as the standard treatment. Diverse clinicopathological and molecular features are major obstacles to accurate predict survival and evaluate the efficacy of chemotherapy or radiotherapy. Reliable prognostic biomarkers are urgently needed for postoperative GBM patients.MethodsThe protein coding genes (PCGs) and long non coding RNA (lncRNA) gene expression profiles of 233 GBM postoperative patients were obtained from The Cancer Genome Atlas (TCGA) TANRIC and Gene Expression Omnibus (GEO) database. We randomly divided the TCGA set into a training (n 76) and a test set (n 77) and used GSE7696 (n 80) as an independent validation set. Survival analysis and the random survival forest algorithm were performed to screen survival associated signature.ResultsSix PCGs (EIF2AK3, EPRS, GALE, GUCY2C, MTHFD2, RNF212) and five lncRNAs (CTD 2140B24.6, LINC02015, AC068888.1, CERNA1, LINC00618) were screened out by a risk score model and formed a PCG lncRNA signature for its predictive power was strongest (AUC 0.78 in the training dataset) The PCG lncRNA signature could divide patients into high risk or low risk group with significantly different survival (median 7.47 vs. 18.27 months, log rank test P 0.001) in the training dataset. Similar result was observed in the test dataset (median 11.40 vs. 16.80 months, log rank test P 0.001) and the independent set (median 8.93 vs. 16.22 months, log rank test P 0.007) Multivariable Cox regression analysis verified that it was an independent prognostic factor for the postsurgical patients with GBM. Compared with IDH mutation status, O (6) methylguanine DNA methyltransferase promoter methylation status and age, the signature was proved to have a superior predictive power. And stratified analysis found that the signature could further separated postoperative GBM patients who received TMZ chemoradiation into high and low risk groups in TCGA and GEO dataset.ConclusionsThe PCG lncRNA signature was a novel prognostic marker to predict survival and TMZ chemoradiation response in GBM patients after surgery.

title: A 63 signature genes prediction system is effective for glioblastoma prognosis
abstract: The present study aimed to explore possible prognostic marker genes in glioblastoma (GBM) Differentially expressed genes (DEGs) were screened by comparing microarray data of tumor and normal tissue samples from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) dataset GSE22866. Subsequently, the prognosis associated DEGs were screened via Cox regression analysis, followed by construction of gene/protein/pathway interaction networks of these DEGs by calculating the correlation coefficient between the DEGs. Next, a prognostic prediction system was constructed using Bayes discriminant analysis, which was validated by the microarray data of samples from patients with good and bad prognosis from the TCGA and Chinese Glioma Genome Atlas (CGGA) as well as the GEO dataset. Finally, a co expression network of the signature genes in the prediction system was constructed in combination with the significant pathways. A total of 288 overlapping DEGs (false discovery rate <0.5 and |log2 of fold change|>1) were screened, 123 of which were identified to be associated with the prognosis of GBM patients. The co expression network of these prognosis associated DEGs included 1405 interactions and 112 DEGs, and 6 functional modules were identified in the network. The prognostic prediction system was comprised of 63 signature genes with a specificity value of 0.929 and a sensitivity value of 0.948. GBM samples with good and bad prognosis in the TCGA, CGGA and GEO datasets were distinguishable by these signature genes (P=1.33x10 6, 1.63x10 4 and 0.00534, respectively) The co expression network of signature genes with significant pathways was comprised of 56 genes and 361 interactions. Protein kinase Cg (PRKCG) protein kinase Cb (PRKCB) and calcium/calmodulin dependent protein kinase IIa (CAMK2A) were important genes in the network, and based on the expression of these genes, it was possible to distinguish between samples with significantly different survival risks. In the present study, an effective prognostic prediction system for GBM patients was constructed and validated. PRKCG, PRKCB and CAMK2A may be potential prognostic factors for GBM.

title: A long non coding RNA signature in glioblastoma multiforme predicts survival
abstract: Long non coding RNAs (lncRNAs) represent the leading edge of cancer research, and have been implicated in cancer biogenesis and prognosis. We aimed to identify lncRNA signatures that have prognostic values in glioblastoma multiforme (GBM) Using a lncRNA mining approach, we performed lncRNA expression profiling in 213 GBM tumors from The Cancer Genome Atlas (TCGA) randomly divided into a training (n=107) and a testing set (n=106) We analyzed the associations between lncRNA signatures and clinical outcome in the training set, and validated the findings in the testing set. We also validated the identified lncRNA signature in another two independent GBM data sets from Gene Expression Omnibus (GEO) which contained specimens from 68 and 101 patients, respectively. We identified a set of six lncRNAs that were significantly associated with the overall survival in the training set (P<=0.01) Based on this six lncRNA signature, the training set patients could be classified into high risk and low risk subgroups with significantly different survival (HR=2.13, 95% CI=1.38 3.29; P=0.001) The prognostic value of this six lncRNA signature was confirmed in the testing set and the two independent data sets. Further analysis revealed that the prognostic value of this signature was independent of age and O 6 methylguanine DNA methyltransferase (MGMT) promoter methylation status. The identification of the prognostic lncRNAs indicates the potential roles of lncRNAs in GBM pathogenesis. This six lncRNA signature may have clinical implications in the subclassification of GBM.

title: Expression Signature of IFN/STAT1 Signaling Genes Predicts Poor Survival Outcome in Glioblastoma Multiforme in a Subtype Specific Manner
abstract: Previous reports have implicated an induction of genes in IFN/STAT1 (Interferon/STAT1) signaling in radiation resistant and prosurvival tumor phenotypes in a number of cancer cell lines, and we have hypothesized that upregulation of these genes may be predictive of poor survival outcome and/or treatment response in Glioblastoma Multiforme (GBM) patients. We have developed a list of 8 genes related to IFN/STAT1 that we hypothesize to be predictive of poor survival in GBM patients. Our working hypothesis that over expression of this gene signature predicts poor survival outcome in GBM patients was confirmed, and in addition, it was demonstrated that the survival model was highly subtype dependent, with strong dependence in the Proneural subtype and no detected dependence in the Classical and Mesenchymal subtypes. We developed a specific multi gene survival model for the Proneural subtype in the TCGA (the Cancer Genome Atlas) discovery set which we have validated in the TCGA validation set. In addition, we have performed network analysis in the form of Bayesian Network discovery and Ingenuity Pathway Analysis to further dissect the underlying biology of this gene signature in the etiology of GBM. We theorize that the strong predictive value of the IFN/STAT1 gene signature in the Proneural subtype may be due to chemotherapy and/or radiation resistance induced through prolonged constitutive signaling of these genes during the course of the illness. The results of this study have implications both for better prediction models for survival outcome in GBM and for improved understanding of the underlying subtype specific molecular mechanisms for GBM tumor progression and treatment response.

title: Genomic profiling of a Hepatocyte growth factor dependent signature for MET targeted therapy in glioblastoma
abstract: BackgroundConstitutive MET signaling promotes invasiveness in most primary and recurrent GBM. However, deployment of available MET targeting agents is confounded by lack of effective biomarkers for selecting suitable patients for treatment. Because endogenous HGF overexpression often causes autocrine MET activation, and also indicates sensitivity to MET inhibitors, we investigated whether it drives the expression of distinct genes which could serve as a signature indicating vulnerability to MET targeted therapy in GBM.MethodsInterrogation of genomic data from TCGA GBM (Student's t test, GBM patients with high and low HGF expression, p 0.00001) referenced against patient derived xenograft (PDX) models (Student's t test, sensitive vs. insensitive models, p 0.005) was used to identify the HGF dependent signature. Genomic analysis of GBM xenograft models using both human and mouse gene expression microarrays (Student's t test, treated vs. vehicle tumors, p 0.01) were performed to elucidate the tumor and microenvironment cross talk. A PDX model with EGFRamp was tested for MET activation as a mechanism of erlotinib resistance.ResultsWe identified a group of 20 genes highly associated with HGF overexpression in GBM and were up or down regulated only in tumors sensitive to MET inhibitor. The MET inhibitors regulate tumor (human) and host (mouse) cells within the tumor via distinct molecular processes, but overall impede tumor growth by inhibiting cell cycle progression. EGFRamp tumors undergo erlotinib resistance responded to a combination of MET and EGFR inhibitors.ConclusionsCombining TCGA primary tumor datasets (human) and xenograft tumor model datasets (human tumor grown in mice) using therapeutic efficacy as an endpoint may serve as a useful approach to discover and develop molecular signatures as therapeutic biomarkers for targeted therapy. The HGF dependent signature may serve as a candidate predictive signature for patient enrollment in clinical trials using MET inhibitors. Human and mouse microarrays maybe used to dissect the tumor host interactions. Targeting MET in EGFRamp GBM may delay the acquired resistance developed during treatment with erlotinib.