Abstract
A universal biomarker panel with the potential to predict high-risk pregnancies or adverse pregnancy outcome does not exist. Transcriptome analysis is a powerful tool to capture differentially expressed genes (DEG), which can be used as biomarker-diagnostic-predictive tool for various conditions in prenatal setting. In search of biomarker set for predicting high-risk pregnancies, we performed global expression profiling to find DEG in Ts21. 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 expression microarrays. Datasets from Ts21 transcriptomic studies from GEO repository were incorporated. DEG were discovered using linear regression modelling and validated using RT-PCR quantification on an independent sample of 16 cases with Ts21 and 32 controls. The classification performance of Ts21 status based on expression profiling was performed using supervised machine learning algorithm and evaluated using a leave-one-out cross validation approach. Global gene expression profiling has revealed significant expression changes between normal and Ts21 samples, which in combination with data from previously performed Ts21 transcriptomic studies, were used to generate a multi-gene biomarker for Ts21, comprising of 9 gene expression profiles. In addition to biomarker’s high performance in discriminating samples from global expression profiling, we were also able to show its discriminatory performance on a larger sample set 2, validated using RT-PCR experiment (AUC=0.97), while its performance on data from previously published studies reached discriminatory AUC values of 1.00. Our results show that transcriptomic changes might potentially be used to discriminate trisomy of chromosome 21 in the prenatal setting. As expressional alterations reflect both, causal and reactive cellular mechanisms, transcriptomic changes may thus have future potential in the diagnosis of a wide array of heterogeneous diseases that result from genetic disturbances.
Highlights
Progressive development and improvement of methods for investigation of global transcriptional alterations in human disease is enabling reproducible and consistent selection of genes that best differentiate samples originating from disease-affected or healthy individuals [1]
Transcriptome is a highly complex and dynamic system, difficult to model with classical approaches, it does present a landscape where manifold pathogenic and reactive processes occurring in disease may be detected
In this study we demonstrate the potential of gene expression signature, consisting of 9 genes, in prediction of Ts21 status in the prenatal setting
Summary
Progressive development and improvement of methods for investigation of global transcriptional alterations in human disease is enabling reproducible and consistent selection of genes that best differentiate samples originating from disease-affected or healthy individuals [1]. Transcriptome is a highly complex and dynamic system, difficult to model with classical approaches, it does present a landscape where manifold pathogenic and reactive processes occurring in disease may be detected. As transcriptional regulation results from genetic as well as environmental influences, transcriptomics present a valuable opportunity for the development of a heterogeneous diagnostic tool for diseases ranging from those of clear genetic aetiology to those of complex unexplained aetiology. As there already are successful implementations of expression biomarkers into clinical practice [2] we opted to investigate and demonstrate the feasibility of expression biomarkers to predict high-risk pregnancy status on Ts21 as a model of high-risk pregnancy. Several global gene expression studies of Ts21 demonstrated extensive changes in expression of chromosome 21 (HSA21) and non- chromosome 21 (nonHSA21) genes [3,4,5,6,7,8,9,10]
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