BJOG : an international journal of obstetrics and gynaecology · 2020
Non-invasive prediction of fetal growth restriction by whole-genome promoter profiling of maternal plasma DNA: a nested case-control study.
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Xu C, Guo Z, Zhang J, Lu Q, Tian Q, Liu S, Li K, Wang K, Tao Z, Li C, Lv Z, Zhang Z, Yang X, Yang F
Аннотация
OBJECTIVE: To predict fetal growth restriction (FGR) by whole-genome promoter profiling of maternal plasma.
DESIGN: Nested case-control study.
SETTING: Hospital-based.
POPULATION OR SAMPLE: 810 pregnancies: 162 FGR cases and 648 controls.
METHODS: We identified gene promoters with a nucleosome footprint that differed between FGR cases and controls based on maternal plasma cell-free DNA (cfDNA) nucleosome profiling. Optimal classifiers were developed using support vector machine (SVM) and logistic regression (LR) models.
MAIN OUTCOME MEASURES: Genes with differential coverages in promoter regions through the low-coverage whole-genome sequencing data analysis among FGR cases and controls. Receiver operating characteristic (ROC) analysis (area under the curve [AUC], accuracy, sensitivity and specificity) was used to evaluate the performance of classifiers.
RESULTS: Through the low-coverage whole-genome sequencing data analysis of FGR cases and controls, genes with significantly differential DNA coverage at promoter regions (-1000 to +1000 bp of transcription start sites) were identified. The non-invasive 'FGR classifier 1' (CFGR 1) had the highest classification performance (AUC, 0.803; 95% CI 0.767-0.839; accuracy, 83.2%) was developed based on 14 genes with differential promoter coverage using a support vector machine.
CONCLUSIONS: A promising FGR prediction method was successfully developed for assessing the risk of FGR at an early gestational age based on maternal plasma cfDNA nucleosome profiling.
TWEETABLE ABSTRACT: A promising FGR prediction method was successfully developed, based on maternal plasma cfDNA nucleosome profiling.
DESIGN: Nested case-control study.
SETTING: Hospital-based.
POPULATION OR SAMPLE: 810 pregnancies: 162 FGR cases and 648 controls.
METHODS: We identified gene promoters with a nucleosome footprint that differed between FGR cases and controls based on maternal plasma cell-free DNA (cfDNA) nucleosome profiling. Optimal classifiers were developed using support vector machine (SVM) and logistic regression (LR) models.
MAIN OUTCOME MEASURES: Genes with differential coverages in promoter regions through the low-coverage whole-genome sequencing data analysis among FGR cases and controls. Receiver operating characteristic (ROC) analysis (area under the curve [AUC], accuracy, sensitivity and specificity) was used to evaluate the performance of classifiers.
RESULTS: Through the low-coverage whole-genome sequencing data analysis of FGR cases and controls, genes with significantly differential DNA coverage at promoter regions (-1000 to +1000 bp of transcription start sites) were identified. The non-invasive 'FGR classifier 1' (CFGR 1) had the highest classification performance (AUC, 0.803; 95% CI 0.767-0.839; accuracy, 83.2%) was developed based on 14 genes with differential promoter coverage using a support vector machine.
CONCLUSIONS: A promising FGR prediction method was successfully developed for assessing the risk of FGR at an early gestational age based on maternal plasma cfDNA nucleosome profiling.
TWEETABLE ABSTRACT: A promising FGR prediction method was successfully developed, based on maternal plasma cfDNA nucleosome profiling.