Expression signature as a biomarker for prenatal diagnosis of trisomy 21

• Published in: PloS one Vol. 8; no. 9; p. e74184
• Main Authors: Volk, Marija, Maver, Aleš, Lovrečić, Luca, Juvan, Peter, Peterlin, Borut
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 16.09.2013; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Bioinformatics; Biomarkers; Chromosome 21; Chromosomes; Chromosomes, Human, Pair 21 - genetics; Constitution; Diagnosis; Diagnostic software; Diagnostic systems; Disease; DNA microarrays; Down syndrome; Down Syndrome - diagnosis; Down Syndrome - genetics; Female; Fetuses; Fibroblasts; Gene expression; Gene Expression Profiling - methods; Genes; Genetics; Genomes; Genomics; Group dynamics; Gynecology; Humans; Learning algorithms; Machine learning; Medical diagnosis; Obstetrics; Oligonucleotide Array Sequence Analysis; Performance evaluation; Performance prediction; Polymerase chain reaction; Pregnancy; Pregnancy complications; Pregnant women; Prenatal diagnosis; Prenatal Diagnosis - methods; Prognosis; Regression analysis; Studies; Trisomy; Trisomy - genetics; Slovenia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0074184

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.