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Circulating miRNA microarray data from breast cancer patients

ABSTRACT: Independent studies have reported that circulating miRNAs have the potential as biomarkers; however, no consolidated guidelines for the discovery process are available. We developed a pipeline using innovative applications of existing bioinformatics methods to (1) face the inapplicability of the classical normalization methods, (2) detect global differences of miRNA distributions between the comparison classes and (3) develop a 'robust’ classifier.

ORGANISM(S): Homo sapiens

PROVIDER: GSE59993 | GEO | 2014/08/01

SECONDARY ACCESSION(S): PRJNA257226

REPOSITORIES: GEO

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Circulating miRNA microarray data from breast cancer patients

Project description:Independent studies have reported that circulating miRNAs have the potential as biomarkers; however, no consolidated guidelines for the discovery process are available. We developed a pipeline using innovative applications of existing bioinformatics methods to (1) face the inapplicability of the classical normalization methods, (2) detect global differences of miRNA distributions between the comparison classes and (3) develop a 'robustM-bM-^@M-^Y classifier. 78 samples (26 hemolyzed and 52 non hemolyzed) were 1:2 paired with caliper matching according to disease status, age at drawing and drawing year, starting from a total of 208 hybridized samples collected between 1987 and 2004 (including 94 paired samples according to tumor ER status, age at drawing, drawing year, time from surgery, 7 additional samples from 'no evidence of disease' (NED) patients, 10 references, 1 healthy donor and 2 replicated samples)

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MS-DAP platform for label-free proteomics uncovers optimal workflows in benchmark datasets and increased sensitivity in analysis of Alzheimer's biomarker data

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Project description:Promising results for prediction of outcome in breast cancer has been obtained by genome wide gene expression profiling. Some studies have suggested that an extensive overtreatment of breast cancer patients might be reduced by risk assessment with gene expression profiling. A patient group hardly examined in these studies is the low-risk patients who are very difficult to differentiate with currently used methods. These patients do not receive adjuvant treatment according to the guidelines of the Danish Breast Cancer Cooperative Group (DBCG). In this study a group of tumors from low-risk patients was examined with gene expression profiling. An intermediate risk group of low-malignant T2 tumors that fulfilled all other low-risk criteria than tumor size was included to increase statistical power. A 32-gene classifier, HUMAC32, was identified and it accurately predicted metastases. The classifier was also validated in an independent group of high-risk tumors resulting in comparable performance of HUMAC32 and a 70-gene classifier developed for this group. Furthermore, the 70-gene signature was tested in the present low- and intermediate-risk samples. The results indicated better performance of HUMAC32 among the low-malignant cancers compared to the 70-gene classifier. This may indicate that although the metastatic potential of a tumor is on the whole determined by the same genes in tumors with different characteristics and risk, some expression patterns have higher predictive power in the low-risk group. Keywords: case-control design

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