Blood dataset - Case-control
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ABSTRACT:
PROVIDER: EGAD00010001062 | EGA |
REPOSITORIES: EGA
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Project description:Peripheral blood samples of patients with acute myocardial infarction were matched with those of control patients to identify possible differences in corresponding gene expression profiles. The controls were matched to cases based on gender, age, status of diabetes mellitus and smoking status. Six months cardiovascular survival status of the cases was used to identify two distinct subgroups among the cases. Linear models for microarray data (‘limma’) were employed to identify differential gene expression. Shrunken centroids technique helped in identifying the subsets of differentially expressed genes with predictive properties in independent samples. Predictive properties were evaluated using bootstrap sampling. Using the limma modeling with the log-fold change threshold of one (clinical significance) and Storey’s q-value approach (statistical significance), sixty transcripts were found to be both clinically and statistically differentially expressed among the cases not surviving the follow-up period relative to controls, while no such transcripts were observed among other surviving cases. The two subgroups of cases exhibited fourteen differentially expressed transcripts. Predictive modeling indicated sixteen out of sixty transcripts to best discriminate between the controls and cases who died during the follow-up period from cardiovascular causes, while for the surviving cases the already non-significant set of transcripts could not be further reduced. Eleven out of fourteen transcripts were found to best discriminate between the two groups of cases using shrunken centroids. The study identified genes, which were differentially expressed during the acute myocardial infarction, including those associated with short-term fatality of the cases.
2014-12-05 | GSE34198 | GEO
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Project description:Peripheral blood samples of patients with acute myocardial infarction were matched with those of control patients to identify possible differences in corresponding gene expression profiles. The controls were matched to cases based on gender, age, status of diabetes mellitus and smoking status. Six months cardiovascular survival status of the cases was used to identify two distinct subgroups among the cases. Linear models for microarray data (M-bM-^@M-^XlimmaM-bM-^@M-^Y) were employed to identify differential gene expression. Shrunken centroids technique helped in identifying the subsets of differentially expressed genes with predictive properties in independent samples. Predictive properties were evaluated using bootstrap sampling. Using the limma modeling with the log-fold change threshold of one (clinical significance) and StoreyM-bM-^@M-^Ys q-value approach (statistical significance), sixty transcripts were found to be both clinically and statistically differentially expressed among the cases not surviving the follow-up period relative to controls, while no such transcripts were observed among other surviving cases. The two subgroups of cases exhibited fourteen differentially expressed transcripts. Predictive modeling indicated sixteen out of sixty transcripts to best discriminate between the controls and cases who died during the follow-up period from cardiovascular causes, while for the surviving cases the already non-significant set of transcripts could not be further reduced. Eleven out of fourteen transcripts were found to best discriminate between the two groups of cases using shrunken centroids. The study identified genes, which were differentially expressed during the acute myocardial infarction, including those associated with short-term fatality of the cases. The genome-wide study used matched case-control design, with 97 samples of 90 patients in three disease groups. Matched control samples were used and several (7) technical replicates were performed.
2014-12-05 | E-GEOD-34198 | biostudies-arrayexpress
Project description:blood-based gene expression from breast cancer cases
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2013-12-12 | E-GEOD-53179 | biostudies-arrayexpress
Project description:The purpose of this study was to improve prediction of patients at high-risk for metastatic disease utilizing a nested case-control design that uniquely enables enrichment for relevant phenotypes. We identified all women diagnosed with primary breast cancer from January 1, 1997, to December 31, 2005, in the Stockholm health care region. Patients developing distant metastatic disease (cases) were selected and controls (free from distant disease) were randomly matched by adjuvant therapy, age and calendar period at diagnosis. The nested case-control study included 768 study subjects with clinical information and gene expression arrays (Human Cancer G110). Study subjects were randomly and equally divided into training set (discovery) or testing (validation) set. Metastatic onset prediction was then compared including either clinical variables only or combining clinical and genetic information. Differentially expressed genes and pathways between cases and controls included a wide-spectrum of well known as well as candidate regulators of the metastatic cascade.
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