Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA156819 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Understanding the molecular events in non-small cell lung cancer (NSCLC) is essential to improve early diagnosis and treatment for this disease. We examined the effect of chromosomal copy number changes on gene expression in resected NSCLC patients. We identified a deletion on 14q32.2-33 as a common alteration in NSCLC (44%), which significantly influenced gene expression for HSP90AA1, residing on 14q32. This deletion was correlated with better overall and recurrence free survival (P=0.008 and P=0.004, respectively) and survival was also longer in patients whose tumors had low expression levels of HSP90AA1. We extended the analysis to an independent validation set of 140 resected NSCLC patients, and confirmed low HSP90AA1 expression to be significantly related with overall survival and recurrence free survival (P=0.003 and P=0.007, respectively). In vitro treatment with an HSP90 inhibitor had potent antiproliferative activity in NSCLC cell lines. We suggest that targeting HSP90 will have clinical impact for NSCLC patients. Keywords: Array CGH gene expression integration
2007-08-01 | GSE7878 | GEO
Project description:Unique MicroRNA Expression Signature Predicts Survival, Metastases and Recurrence of HCC
| PRJNA99101 | ENA
Project description:We developed a 33-gene signature that is strongly correlated to the time to recurrence in non-small cell lung cancer (NSCLC). The signature was validated retrospectively in 5 cohorts of 972 NSCLC patients and in one prospective study of 111 NSCLC Stage IA patients. In all cohorts, and all stages of the disease, the signature identified a rare, aggressive tumor type that had a high proportion of recurrence after surgery and a median survival of 35 months (95% C.I.: 19-58). This tumor type forms a separate cluster in an analysis of the expression of the 33 genes in patient tumors. The signature is associated with cellular processes required by rapidly growing and spreading tumors: cell migration and invasion, vascularization, and response to hypoxia. The signature also identifies patients with good prognosis (median survival 114 months, (95% C.I.: 85-160), and intermediate prognosis (median survival 61 months (95% C. I.: 50-73). The signature is quite robust and works on tumor samples archived in RNAlater, Tissue-Tek, or formalin-fixed and paraffin embedded. 156 samples -------------------------------- *** Submitter has not provided information such as time to recurrence. Thus, the data is incomplete.
2016-02-11 | E-GEOD-77803 | biostudies-arrayexpress
Project description:Prediction of Recurrence-Free Survival in Postoperative NSCLC Patients—a Useful Prospective Clinical Practice
| PRJNA102281 | ENA
Project description:Multidrug Resistance-Linked Gene Signature Predicts Overall Survival of Patients with Primary Ovarian Serous Carcinoma
| PRJNA143681 | ENA
Project description:Background: Current histopathological methods are inadequate for predicting outcome and recurrence in patients with non-small cell lung carcinoma (NSCLC) after surgery. In this study, we investigated the use of gene expression signatures to predict outcome and metastasis in lung cancer patients. Methods: Gene expression was studied by microarray and the real-time reverse transcriptase polymerase chain reaction (RT-PCR) in normal and lung tumor tissue of 188 NSCLC patients who underwent surgical resection. The 5 cancer-related genes and 1 reference gene expression levels measureed by real-time RT-PCR were used in a prospectively defined algorithm to determine the risk for each patient. Finally, we used an independent cohort to verify the 5 gene-based predictive model derived from decision tree analysis. Results: The 5 gene-based decision tree model was able to predict the prognosis. The recurrence rate at 36 months was 53% in the low-risk group versus 83% in the high-risk group (P=0.002). The 5 gene-based model could also predict overall survival (P<0.001). In multivariate analysis, the decision tree model predicted that high-low dichotomy and stage were both significant for recurrence. In addition, it could also predict metastasis and survival of NSCLC patients within the stage I-II subgroups. A similar result was found using an independent cohort of NSCLC patients. The high-risk patients had a significantly poorer overall survival than the low-risk patients (P=0.005). We also found distinct gene signatures which could distinguish between NSCLC, and normal tissue and histology subtypes. Conclusions: A gene expression signature can predict metastasis and survival of NSCLC patients. Keywords: Survival and metastasis analysis
2006-06-07 | GSE4882 | GEO
Project description:EGFR-mutated non-small cell lung cancers bear hallmarks including sensitivity to EGFR inhibitors, and low proliferation, and increased MET. However, the biology of EGFR dependence is still poorly understood. Using a training cohort of chemo-naive lung adenocarcinomas, we have developed a 72-gene signature that predicts (i) EGFR mutation status in four independent datasets; (ii) sensitivity to erlotinib in vitro; and (iii) improved survival, even in the wild-type EGFR subgroup. The signature includes differences associated with enhanced receptor tyrosine kinase (RTK) signaling, such as increased expression of endocytosis-related genes, decreased phosphatase levels, decreased expression of proliferation-related genes, increased folate receptor-1 (FOLR1) (a determinant of pemetrexed response), and higher levels of MACC1 (which we identify as a regulator of MET in EGFR-mutant NSCLC). Those observations provide evidence that the EGFR-mutant phenotype is associated with alterations in the cellular machinery that links the EGFR and MET pathways and create a permissive environment for RTK signaling. We have developed a gene expression signature that predicts (i) EGFR mutation in chemo-naive and, to a lesser extent, in chemo-refractory NSCLC patients; (ii) EGFR TKI response in vitro; and (iii) survival in wild-type EGFR patients. The signature also identifies novel features of EGFR mutant NSCLC including increased levels of endocytosis-related genes and MACC1, which appears be an EGFR mutant associated regulator of MET. Gene expression profiles were measured in 124 core biopsies from patients with refractory non-small cell lung cancer in the Biomarker-integrated Approaches of Targeted Therapy for Lung Cancer Elimination (BATTLE) trial. We used the BATTLE dataset to test an EGFR-mutation gene expression signature trained in chemo-naive lung adenocarcinoma. The signature was computed as an index, called EGFR index.
2014-06-01 | E-GEOD-31852 | biostudies-arrayexpress
Project description:Study to identify genes associated with NSCLC recurrence in patients not receiving adjuvant chemotherapy. Purpose: Recent clinical trials suggest improvement in survival with adjuvant chemotherapy in non-small cell lung cancer (NSCLC). This study's aim is to identify genes associated with NSCLC recurrence in patients not receiving adjuvant chemotherapy. Experimental design: Banked NSCLC tumors of patients who underwent resection of stage Ia-IIIb disease were identified. Patients were stratified into two groups: recurrent (R, n=11) or non-recurrent (NR, n=16) 2 years after surgery. Microarray profiling and Cox multivariate analysis were performed. Conclusion: Increased CYP3A5 gene expression correlates with NSCLC recurrence and promotes proliferation through mechanisms that may involve, in part, CYP3A5 epoxygenase activity. Experiment Overall Design: comparison of gene expression profiles for recurrent and non-recurrent cancer
2009-12-14 | E-GEOD-9971 | biostudies-arrayexpress
Project description:We developed a 33-gene signature that is strongly correlated to the time to recurrence in non-small cell lung cancer (NSCLC). The signature was validated retrospectively in 5 cohorts of 972 NSCLC patients and in one prospective study of 111 NSCLC Stage IA patients. In all cohorts, and all stages of the disease, the signature identified a rare, aggressive tumor type that had a high proportion of recurrence after surgery and a median survival of 35 months (95% C.I.: 19-58). This tumor type forms a separate cluster in an analysis of the expression of the 33 genes in patient tumors. The signature is associated with cellular processes required by rapidly growing and spreading tumors: cell migration and invasion, vascularization, and response to hypoxia. The signature also identifies patients with good prognosis (median survival 114 months, (95% C.I.: 85-160), and intermediate prognosis (median survival 61 months (95% C. I.: 50-73). The signature is quite robust and works on tumor samples archived in RNAlater, Tissue-Tek, or formalin-fixed and paraffin embedded.
2016-02-11 | GSE77803 | GEO
Project description:Study aims: to identify critical genes involved in non-small cell lung cancer (NSCLC) pathogenesis that may lead to a more complete understanding of this disease and identify novel molecular targets for use in the development of more effective therapies. Methods: Both transcriptional and genomic profiling were performed on 69 resected NSCLC specimens and results correlated with mutational analyses and clinical data to identify genetic alterations associated with groups of interest. Results: Combined analyses identified specific patterns of genetic alteration associated with adenocarcinoma vs. squamous differentiation; KRAS mutation; TP53 mutation, metastatic potential and disease recurrence and survival. Amplification of 3q was associated with mutations in TP53 in adenocarcinoma. A prognostic signature for disease recurrence, reflecting KRAS pathway activation, was validated in an independent test set. Conclusions: These results may provide the first steps in identifying new predictive biomarkers and targets for novel therapies, thus improving outcomes for patients with this deadly disease. Transcriptional profiling of 82 samples of NSCLC using Peter MacCallum in-house 10,500 element cDNA microarray. RNA from 11 pooled cell lines used as reference for each sample. Replicate of 6 samples included.
2010-11-27 | E-GEOD-25326 | biostudies-arrayexpress