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Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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Characterization of gene expression profiles of gastric cancer in MD Anderson Cancer Center cohort

ABSTRACT: Clinical heterogeneity of gastric cancer reflected in unequal outcome of treatment is poorly defined in molecular level, and molecular subtypes and their associated biomarkers have not been established to improve prognostification and treatment of gastric cancer. Using microarray technologies, we analyzed gene expression profiling data from patients with advanced gastric cancer and uncovered potential prognostic subtypes and identify gene expression signature associated with prognosis. Using microarray technologies, we analyzed gene expression profiling data from patients with advanced gastric cancer and uncovered potential prognostic subtypes and identify gene expression signature associated with prognosis.

ORGANISM(S): Homo sapiens

SUBMITTER: Sangbae Kim

PROVIDER: E-GEOD-28541 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Characterization of gene expression profiles of gastric cancer in MD Anderson Cancer Center cohort

Project description:Clinical heterogeneity of gastric cancer reflected in unequal outcome of treatment is poorly defined in molecular level, and molecular subtypes and their associated biomarkers have not been established to improve prognostification and treatment of gastric cancer. Using microarray technologies, we analyzed gene expression profiling data from patients with advanced gastric cancer and uncovered potential prognostic subtypes and identify gene expression signature associated with prognosis.

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Gene expression signature-based novel prognostic risk score in gastric cancer

Project description:Despite continual efforts to establish pre-operative prognostic model of gastric cancer by using clinical and pathological parameters, a staging system that reliably separates patients with early and advanced gastric cancer into homogeneous groups with respect to prognosis does not exist. With use of microarray and quantitative RT-PCR technologies, we exploited series of experiments in combination with complementary data analyses on tumor specimens from 161 gastric cancer patients. Various statistical analyses were applied to gene expression data to uncover subgroups of gastric cancer, to identify potential biomarkers associated with prognosis, and to construct molecular predictor of risk from identified prognostic biomarkers.Two subgroups of gastric cancer with strong association with prognosis were uncovered. The robustness of prognostic gene expression signature was validated in independent patient cohort with use of support vector machines prediction model. For easy translation of our finding to clinics, we develop scoring system based on expression of six genes that can predict the likelihood of recurrence after curative resection of tumors. In multivariate analysis, our novel risk score was an independent predictor of recurrence (P=0.004) in cohort of 96 patients, and its robustness was validated in two other independent cohorts. We identified novel prognostic subgroups of gastric cancer that are distinctive in gene expression patterns. Six-gene signature and risk score derived from them has been validated for predicting the likelihood of survival at diagnosis. 65 primary gastric adenocarcinoma, 6 GIST and 19 surrounding normal fresh frozen tissues were used for microarray. All the tissues were obtained after curative resection after pathologic confirm at Yonsei cancer center(Seoul, Korea). Microarray experiment and data analysis were done at Dept. of systems biology, MDACC DNA microarray (Illumina human V3)

2011-07-18 | E-GEOD-13861 | biostudies-arrayexpress

Gene expression profile prospectively predicts peritoneal relapse after curative surgery of gastric cancer

Project description:Purpose: Our study aimed to disclose the specific gene expression profile representing peritoneal relapses inherent in primary gastric cancers and to identify patients at high risk of peritoneal relapse in a prospective study on the basis of the molecular prediction. Experimental Design: RNA samples from 141 primary gastric cancer tissues after curative surgery were profiled using oligonucleotide microarrays covering 30,000 human probes. Firstly we constructed molecular prediction system and validated the robustness and prognostic validity of the analysis by 500 times multiple random sampling in 56 retrospective set consisting of 38 relapse free and 18 peritoneal relapse patients. Secondly we applied this prediction to 85 prospective set to assess the predictive accuracy and prognostic validity. Results: In retrospective phase, 500 times multiple random sampling analysis yielded 68% predictive accuracy in average and 22 gene expression profile associated with peritoneal relapse was identified. This prediction could identify significantly poor prognostic patients. In prospective phase, the molecular prediction yielded 76.9% overall accuracy. KaplanâMeier analysis with peritoneal relapse free survival showed a significant difference between âgood signature groupâ and âpoor signature groupâ (Log-rank p=0.0017). Multivariate analysis by Cox regression hazards model revealed that the molecular prediction was the only independent peritoneal relapse prognostic factor. Conclusions: Gene expression profile inherent in primary gastric cancer tissues can be useful to predict peritoneal relapse prospectively after curative surgery and individualize postoperative management to improve the prognosis of advanced gastric cancers. Of 141 samples, 56 represented the retrospective phase and 85 represented the prospective phase.

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Project description:We sought to illustrate molecular subtypes associated with clinical prognosis and to identify genetic aberrations for potential targeted therapeutics through comprehensive whole-genome analysis of 131 Chinese gastric cancer tissue specimens using whole-genome array CGH. We profiled 131 Chinese gastric cancer genomes using Agilent 244K array CGH technology. 70 samples Among them were further profiled by Affymetrix Hu133Plus2 arrays for mRNA expression.

2014-05-06 | E-GEOD-57302 | biostudies-arrayexpress

Overexpression of Snail is associated with lymph node metastasis and poor prognosis in patients with gastric cancer

Project description:Epithelial-mesenchymal transition (EMT) is in the highlights as a significant role in tumor progression and invasion. Snail was known as the one of regulators of EMT in various malignant tumors. The aim of this study was to investigate the effect of Snail on invasiveness/migratory ability of gastric cancer cell lines and clinicopathological and prognostic significance of Snail over-expression using immunohistochemistry in tissue microarray of 314 gastric adenocarcinomas (GC). Differential gene expression in Snail over-expressed GC was investigated using cDNA microarray analysis. Silencing of Snail by ShRNA induced decreased of invasion, migration of gastric cancer cell lines. In contrast, over-expression of Snail induced increased invasiveness and migratory ability of gastric cancer cell lines in accordance increase of VEGF and MMP11. Furthermore, the over-expression of Snail (?75% nuclear staining of Snail) was significantly associated with tumor progression (p<0.0001), lymph node metastases (p=0.002), lymphovascular invasion (p=0.002), and perineural invasion (p=0.002) in 314 GC patient. Snail over-expression was also associated with poor prognosis (shorter survival) in GC patients (p=0.023). cDNA microarray revealed 213 differential expressed genes in Snail over-expressed GC tissues, including genes related to metastasis, invasion. Based on above results, it was suggested that Snail plays a significant role in invasiveness/migratory ability of GCs. In addition, Snail might be used to predictive biomarker for evaluation of prognosis or aggressiveness in GCs. 48 primary gastric adenocarcinoma fresh frozen tissues were used for microarray. All the tissues were obtained after curative resection after pathologic confirm at Pusan National University Hospital (PNUH, Busan, Korea) and Cheonnam University Hospital (CNUH, Cheonnam, Korea). Microarray experiment and data analysis were done at Cancer research institute, PNUH, Busan, Korea.

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