Dataset Information

A standardized microarray assay for the independent gene expression markers in AML: EVI1 and BAALC.

ABSTRACT: High levels of BAALC, ERG, EVI1 and MN1 expression have been associated with shorter overall survival in AML but standardized and clinically validated assays are lacking. We have therefore developed and optimized an assay for standardized detection of these prognostic genes for patients with intermediate cytogenetic risk AML. In a training set of 147 intermediate cytogenetic risk cases we performed cross validations at 5 percentile steps of expression level and observed a bimodal significance profile for BAALC expression level and unimodal significance profiles for ERG and MN1 levels with no statistically significant cutoff points near the median expression level of BAALC, ERG or MN1. Of the possible cutoff points for expression levels of BAALC, ERG and MN1, just the 30th and 75th percentile of BAALC expression level and the 30th percentile of MN1 expression level cutoff points showed clinical significance. Of these only the 30th percentile of BAALC expression level reproduced in an independent verification (extended training) data set of 242 cytogenetically normal AML cases and successfully validated in an external cohort of 215 intermediate cytogenetic risk AML cases. Finally, we show independent prognostic value for high EVI1 and low BAALC in multivariate analysis with other clinically relevant molecular AML markers. We have developed a highly standardized molecular assay for the independent gene expression markers EVI1 and BAALC.

SUBMITTER: Brand J

PROVIDER: S-EPMC3605258 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature

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A standardized microarray assay for the independent gene expression markers in AML: EVI1 and BAALC.

Brand Jaap J van Vliet Martin H MH de Best Leonie L Valk Peter Jm PJ Viëtor Henk E HE Löwenberg Bob B van Beers Erik H EH

Experimental hematology & oncology 20130306 1

High levels of BAALC, ERG, EVI1 and MN1 expression have been associated with shorter overall survival in AML but standardized and clinically validated assays are lacking. We have therefore developed and optimized an assay for standardized detection of these prognostic genes for patients with intermediate cytogenetic risk AML. In a training set of 147 intermediate cytogenetic risk cases we performed cross validations at 5 percentile steps of expression level and observed a bimodal significance pr ...[more]

PMID: 23497432

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Project description:Gene microarray technology is an effective tool to investigate the simultaneous activity of multiple cellular pathways from hundreds to thousands of genes. However, because data in the colossal amounts generated by DNA microarray technology are usually complex, noisy, high-dimensional, and often hindered by low statistical power, their exploitation is difficult. To overcome these problems, two kinds of unsupervised analysis methods for microarray data: principal component analysis (PCA) and independent component analysis (ICA) have been developed to accomplish the task. PCA projects the data into a new space spanned by the principal components that are mutually orthonormal to each other. The constraint of mutual orthogonality and second-order statistics technique within PCA algorithms, however, may not be applied to the biological systems studied. Extracting and characterizing the most informative features of the biological signals, however, require higher-order statistics.ICA is one of the unsupervised algorithms that can extract higher-order statistical structures from data and has been applied to DNA microarray gene expression data analysis. We performed FastICA method on DNA microarray gene expression data from Alzheimer's disease (AD) hippocampal tissue samples and consequential gene clustering. Experimental results showed that the ICA method can improve the clustering results of AD samples and identify significant genes. More than 50 significant genes with high expression levels in severe AD were extracted, representing immunity-related protein, metal-related protein, membrane protein, lipoprotein, neuropeptide, cytoskeleton protein, cellular binding protein, and ribosomal protein. Within the aforementioned categories, our method also found 37 significant genes with low expression levels. Moreover, it is worth noting that some oncogenes and phosphorylation-related proteins are expressed in low levels. In comparison to the PCA and support vector machine recursive feature elimination (SVM-RFE) methods, which are widely used in microarray data analysis, ICA can identify more AD-related genes. Furthermore, we have validated and identified many genes that are associated with AD pathogenesis.We demonstrated that ICA exploits higher-order statistics to identify gene expression profiles as linear combinations of elementary expression patterns that lead to the construction of potential AD-related pathogenic pathways. Our computing results also validated that the ICA model outperformed PCA and the SVM-RFE method. This report shows that ICA as a microarray data analysis tool can help us to elucidate the molecular taxonomy of AD and other multifactorial and polygenic complex diseases.

Dataset Information

A standardized microarray assay for the independent gene expression markers in AML: EVI1 and BAALC.

Publications

A standardized microarray assay for the independent gene expression markers in AML: EVI1 and BAALC.

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