Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA115795 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Array CGH analysis was done with 56 primary gastric cancers to elucidate prognostic biomarkers on the BAC basis. Using the extracted genomic DNA from 56 primary gastric cancers, array CGH was done to elucidate the prognostic biomarkers.
2010-08-10 | E-GEOD-15436 | biostudies-arrayexpress
Project description:Array CGH analysis was done with 56 primary gastric cancers to elucidate prognostic biomarkers on the BAC basis.
2010-08-10 | GSE15436 | GEO
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.
2010-05-23 | E-GEOD-15081 | biostudies-arrayexpress
Project description:Genome-wide mRNA expression profiles of 56 primary gastric tumors from the Singapore patient cohort, batch B. Like many cancers, gastric adenocarcinomas (gastric cancers) show considerable heterogeneity between patients. Thus, there is intense interest in using gene expression profiles to discover subtypes of gastric cancers with particular biological properties or therapeutic vulnerabilities. Identification of such subtypes could generate insights into the mechanisms of cancer progression or lay the foundation for personalized treatments. Here we report a robust gene-expression-based clustering of a large collection of gastric adenocarcinomas (with GSE15459) from Singaporean patients. Profiling of 56 primary gastric tumors on Affymetrix GeneChip Human Genome U133 Plus 2.0 Array. All tumors were collected with approvals from the National Cancer Centre, Singapore; the Research Ethics Review Committee; and signed patient informed consent.
2014-09-05 | E-GEOD-34942 | biostudies-arrayexpress
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. Kaplan–Meier analysis with peritoneal relapse free survival showed a significant difference between ‘good signature group’ and ‘poor signature group’ (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.
2009-03-04 | GSE15081 | GEO
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.
2010-11-30 | E-GEOD-17154 | biostudies-arrayexpress
Project description:In this study, we investigated CNAs of 59 tumor samples from 27 patients with submucosal-invasive gastric cancers (SMGC) by 44k oligonucleotide-based array comparative genomic hybridization (array CGH).
2012-01-20 | GSE26800 | GEO
Project description:In current study, we applied array-CGH analysis to detect somatic copy number aberrations across tumor genome to help separate multiple primary lung cancers from metastasis cancers.
2017-02-10 | GSE86607 | GEO
Project description:Genome-wide mRNA expression profiles of 56 primary gastric tumors from the Singapore patient cohort, batch B. Like many cancers, gastric adenocarcinomas (gastric cancers) show considerable heterogeneity between patients. Thus, there is intense interest in using gene expression profiles to discover subtypes of gastric cancers with particular biological properties or therapeutic vulnerabilities. Identification of such subtypes could generate insights into the mechanisms of cancer progression or lay the foundation for personalized treatments. Here we report a robust gene-expression-based clustering of a large collection of gastric adenocarcinomas (with GSE15459) from Singaporean patients.
2014-09-05 | GSE34942 | GEO
Project description:To investigate miRNA expression profiles between gastric cancers plasma and healthy controls plasma,screening the microRNAs that can serve as plasma biomarkers for the early diagnosis of gastric cancer by miRNA array.
2016-09-13 | GSE86822 | GEO