Gastric cancer miRnome
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REPOSITORIES: ENA
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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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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)
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Project description:Field Effects in Gastric Cancer
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Project description:Gene expression profiling of apparently normal gastric tissue (obtained from patients undergoing gastric surgery for Non-gastric cancers), paired normals (obtained from the same stomach as the gastric cancer but confirmed by frozen section not to harbour any tumour cells) and gastric cancer, with an intent to identify genes involved in the malignant transformation of normal gastric mucosa and to identify genes which can be used as biomarkers for early diagnosis and potential targets for treatment Identification of novel prognostic markers using microarray gene expression studies. Keywords: Patient tissue samples Two-dye experiments using Universal control RNA (Stratagene) and RNA from tissues. Biological replicates: Apparently Normal = 5; Paired Normal = 20; Gastric cancers = 24. One replicate per array.
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