Expression data of Human Gastric Adenocarcinoma
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ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Fatima Pasini
PROVIDER: E-GEOD-38749 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Similar Datasets
Project description:Anatomical staging is a critical, although imperfect, instrument to assess gastric cancer prognosis and define indication for surgery and adjuvant therapy. Despite recent advances, treatment results, as a whole, remain less than satisfactory. Thus, biomarkers are sorely needed to improve risk categorization and define new molecular targets for therapy. We used microarrays to identify genes differentially expressed according to prognosis of the patients.
2012-09-01 | GSE38749 | GEO
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.
2011-07-19 | GSE13861 | GEO
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
Project description:The aim of this study is to identify and verify a prognosis set that is able to distinguish patient overall survival status based on whole genome expression profiling. Using Illumina HumanWG-6 beadarray, we analyzed the expression of 47,296 transcripts in two groups of gastric cancer patients who all underwent surgical resection. Thirty-nine of 46 samples in group one were used as a training set to discover candidates for overall survival prediction by using semi-supervised method, resulting in a panel of 10 genes as prognosis markers. This 10-marker set classified each case into a low or a high risk group with significantly different survival times (P = 0.000047) and maintained independent prognostic value in multivariate analysis. These 10 candidates were then verified in a fully independent validation set comprising 33 samples (P = 0.0009). Furthermore, the prognosis was consistent with the classical TNM staging system, and showed even better prediction. Interestingly, six of ten survival markers are ribosomal proteins, suggesting a possible association between deregulation of ribosome-related expression and poor prognosis. In conclusion, by whole genome expression profiling, 10 markers, including 6 ribosomal proteins, for overall survival prognosis of gastric cancer were found and validated, and which may be parallelly used with the TNM system. We investigated the transcriptome profiles of gastric cancer patients with different prognosis. Thirty-nine patients from first batch samples were used to discover prognostic markers and another 33 samples from batch two were used for validation.
2011-07-12 | E-GEOD-21983 | biostudies-arrayexpress
Project description:The paper "Metabolomic Machine Learning Predictor for Diagnosis and Prognosis of Gastric Cancer" addresses the need for non-invasive diagnostic tools for gastric cancer (GC). Traditional methods like endoscopy are invasive and expensive. The authors conducted a targeted metabolomics analysis of 702 plasma samples to develop machine learning models for GC diagnosis and prognosis. The diagnostic model, using 10 metabolites, achieved a sensitivity of 0.905, outperforming conventional protein marker-based methods. The prognostic model effectively stratified patients into risk groups, surpassing traditional clinical models.
I have successfully reproduced the diagnosis model from the paper. This machine learning-based system differentiates GC patients from non-GC controls using metabolomics data from plasma samples analyzed by liquid chromatography-mass spectrometry (LC-MS). The model focuses on 10 metabolites, including succinate, uridine, lactate, and serotonin. Employing LASSO regression and a random forest classifier, the model achieved an AUROC of 0.967, with a sensitivity of 0.854 and specificity of 0.926. This model significantly outperforms traditional diagnostic methods and underscores the potential of integrating machine learning with metabolomics for early GC detection and treatment.
2024-07-23 | MODEL2407230001 | BioModels
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: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.
2012-04-29 | E-GEOD-28541 | 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:The aim of this study is to identify and verify a prognosis set that is able to distinguish patient overall survival status based on whole genome expression profiling. Using Illumina HumanWG-6 beadarray, we analyzed the expression of 47,296 transcripts in two groups of gastric cancer patients who all underwent surgical resection. Thirty-nine of 46 samples in group one were used as a training set to discover candidates for overall survival prediction by using semi-supervised method, resulting in a panel of 10 genes as prognosis markers. This 10-marker set classified each case into a low or a high risk group with significantly different survival times (P = 0.000047) and maintained independent prognostic value in multivariate analysis. These 10 candidates were then verified in a fully independent validation set comprising 33 samples (P = 0.0009). Furthermore, the prognosis was consistent with the classical TNM staging system, and showed even better prediction. Interestingly, six of ten survival markers are ribosomal proteins, suggesting a possible association between deregulation of ribosome-related expression and poor prognosis. In conclusion, by whole genome expression profiling, 10 markers, including 6 ribosomal proteins, for overall survival prognosis of gastric cancer were found and validated, and which may be parallelly used with the TNM system.
2011-07-13 | GSE21983 | GEO
Project description:Gastric cancer is the second most common cause of cancer-related death worldwide. Up to 80% of patients who undergo curative surgical resection develop locoregional or distant recurrence. A recent large-scaled meta-analysis on 3,838 patients from 17 trials has demonstrated survival benefit from adjuvant chemotherapy when compared to surgery alone. Furthermore, INT-0116 study has demonstrated a survival benefit from postoperative chemoradiation therapy with 5-fluorouracil and leucovorin in gastric cancer patients. Despite these advances, 5-year disease-free survival rates remain poor for patients diagnosed with stage III or IV gastric cancer (stage IIIA, 57.6%, stage IIIB, 39.6%; and stage IV 26.3%) underscoring the need for development of new targeted agents. On the other hand those diagnosed with stage Ib/II gastric cancer have moderate 5 year recurrence rate (76.2%) and suggest the presence of significant molecular heterogeneity with varying prognosis. We hypothesized that gene expression of profiling of formalin fixed paraffin embedded tumor (FFPET) samples using whole genome cDNA-mediated Annealing, Selection, Extension, and Ligation (WG-DASL) assay could be used to develop robust prognostic profiles for gastric cancer treated with chemoradiotherapy that are independent of clinicopathological features. We have identified and validated a gene expression signature that predicts recurrence after curative resection. We also performed pathway analyses to delineate aberrant pathways in aggressive gastric cancer which suggest targeted treatment strategies.
2014-03-01 | GSE26253 | GEO