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Expression signature as a biomarker for prenatal diagnosis of trisomy 21

ABSTRACT: To date, a universal biomarker panel with a potential to predict high-risk pregnancies or adverse pregnancy outcome does not exist. Transcriptomic approach, a measure of gene expression levels across the genome, is a powerful tool to capture differentially expressed genes (DEG) in various conditions, including human trisomy of chromosome 21 (Ts21). DEG can be used to design a biomarker set as a diagnostic-predictive tool for various conditions of heterogeneous aetiology in a prenatal setting. In the search of novel biomarker set to predict high-risk pregnancies, we performed global expression profiling to find DEG in Ts21 used as a model. Subsequently we performed targeted validation and diagnostic performance evaluation on a larger group of case and control samples. Initially, transcriptomic profiles of 10 cultivated amniocyte samples with Ts21 and 9 with normal euploid constitution were determined using Agilent 4x44K expression microarrays. DEG were discovered using linear regression modelling implemented in limma package. Datasets from Ts21 transcriptomic studies available at GEO repository were incorporated to select our preliminary top DEG. Subsequently, selected top DEG were validated using RT-PCR quantification on independent sample of 16 cases with Ts21 and 32 controls, as well as new datasets from previously performed expression studies in Ts21. The classification was performed using support vector machine classification kernel and evaluated using leave-one-out cross validation approach.

ORGANISM(S): Homo sapiens

PROVIDER: GSE48051 | GEO | 2013/06/19

SECONDARY ACCESSION(S): PRJNA208796

REPOSITORIES: GEO

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Project description:Motivation: Monitoring, assessment and prediction of environmental risks that chemicals pose demand rapid and accurate diagnostic assays. A variety of toxicological effects have been associated with explosive compounds TNT and RDX. One important goal of microarray experiments is to discover novel biomarkers for toxicity evaluation. We have developed an earthworm microarray containing 15,208 unique oligo probes. Our objective was to identify biomarker genes that can separate earthworm samples into three groups: control, TNT-treated, and RDX-treated. Results: We developed a discriminant analysis and cluster (DAC) pipeline to analyze a 248-array dataset. First, a total of 869 significantly changed genes in response to TNT or RDX exposure were inferred by class comparison statistical algorithms. Then, nine decision tree-based algorithms were applied to generate classification rules and a set of 286 classifier genes. These classifier genes were ranked by their overall weight of significance, and were used to build support vector machines (SVMs). An SVM containing all 286 classifier genes had the highest classification accuracy (91.5%). An unsupervised clustering method was used to cluster the worm samples and results show that the use of the top 100 classifier genes can assign the largest number of worm samples into the three reference clusters obtained by using all the 14,188 filtered genes, suggesting that these top-ranked genes may be potential candidates for biomarkers. This study demonstrates that the DAC pipeline can be used to identify a small set of biomarker genes from high dimensional datasets and generate a reliable SVM classification model for multiple classes. Adult earthworms (E. fetida) were exposed in soil spiked with TNT (0, 6, 12, 24, 48, or 96 mg/kg) or RDX (8, 16, 32, 64, or 128 mg/kg) for 0, 4 or 14 days. The 4-day treatment was repeated with RDX concentration being 2, 4, 8, 16 or 32 mg/kg soil. Each treatment originally had 10 replicate worms with 8-10 survivors at the end of exposure. Total RNA was isolated from the surviving worms. A total of 248 worm RNA samples were hybridized to a custom-designed oligo array using Agilentâs one-color Low RNA Input Linear Amplification Kit. The array contains 15,208 non-redundant 60-mer probes, each targeting a unique E. fetida transcript (Gong et al. 2009). After hybridization and scanning, gene expression data were acquired using Agilentâs Feature Extraction Software (v.9.1.3). The 248-array dataset consists of three worm groups: 32 untreated controls, 96 TNT-treated, and 120 RDX-treated.

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Expression signature as a biomarker for prenatal diagnosis of trisomy 21

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