Abstract：
BACKGROUND: Fetal macrosomia is common occurrence in pregnancy, which is associated with several adverse prognosis both of maternal and neonatal. While, the accuracy of prediction of fetal macrosomia is poor. The aim of this study was to develop a reliable noninvasive prediction classifier of fetal macrosomia.
METHODS: A total of 3600 samples of routine noninvasive prenatal testing (NIPT) data at 12+ 0-27+ 6 weeks of gestation, which were subjected to low-coverage whole-genome sequencing of maternal plasma cell-free DNA (cfDNA), were collected from three independent hospitals. We identified set of genes with significant differential coverages by comparing the promoter profiling between macrosomia cases and controls. We selected genes to develop classifier for noninvasive predicting, by using support vector machine (SVM) and logistic regression models, respectively. The performance of each classifier was evaluated by area under the curve (AUC) analysis.
RESULTS: According to the available follow-up results, 162 fetal macrosomia pregnancies and 648 matched controls were included. A total of 1086 genes with significantly differential promoter profiling were found between pregnancies with macrosomia and controls (p < 0.05). With the AUC as a reference，the classifier based on SVM (CMA-A2) had the best performance, with an AUC of 0.8256 (95% CI: 0.7927-0.8586).
CONCLUSIONS: Our study provides that assessing the risk of fetal macrosomia by whole-genome promoter nucleosome profiling of maternal plasma cfDNA based on low-coverage next-generation sequencing is feasible.
METHODS: A total of 3600 samples of routine noninvasive prenatal testing (NIPT) data at 12+ 0-27+ 6 weeks of gestation, which were subjected to low-coverage whole-genome sequencing of maternal plasma cell-free DNA (cfDNA), were collected from three independent hospitals. We identified set of genes with significant differential coverages by comparing the promoter profiling between macrosomia cases and controls. We selected genes to develop classifier for noninvasive predicting, by using support vector machine (SVM) and logistic regression models, respectively. The performance of each classifier was evaluated by area under the curve (AUC) analysis.
RESULTS: According to the available follow-up results, 162 fetal macrosomia pregnancies and 648 matched controls were included. A total of 1086 genes with significantly differential promoter profiling were found between pregnancies with macrosomia and controls (p < 0.05). With the AUC as a reference，the classifier based on SVM (CMA-A2) had the best performance, with an AUC of 0.8256 (95% CI: 0.7927-0.8586).
CONCLUSIONS: Our study provides that assessing the risk of fetal macrosomia by whole-genome promoter nucleosome profiling of maternal plasma cfDNA based on low-coverage next-generation sequencing is feasible.
摘要：
背景：巨大胎儿常见于妊娠期，这与孕产妇和新生儿的一些不良预后有关。同时,预测巨大胎儿的准确性较差。这项研究的目的是开发一种可靠的胎儿巨大儿的无创预测分类器。
方法：在妊娠12+0-27+6周时，共有3600个样本的常规无创产前检测（NIPT）数据，对母体浆细胞游离DNA（cfDNA）进行低覆盖率全基因组测序，是从三家独立医院收集的。通过比较巨大儿病例和对照之间的启动子概况，我们确定了具有显着差异覆盖的一组基因。我们选择基因来开发非侵入性预测的分类器，通过使用支持向量机(SVM)和逻辑回归模型，分别。通过曲线下面积(AUC)分析评价每个分类器的性能。
结果：根据现有的随访结果，包括162例巨大胎儿妊娠和648例匹配的对照。在患有巨大儿的妊娠和对照组之间发现了总共1086个具有显著差异的启动子谱的基因(p<0.05)。以AUC为参考，基于SVM(CMA-A2)的分类器性能最好，AUC为0.8256（95%CI：0.7927-0.8586）。
结论：我们的研究表明，基于低覆盖下一代测序，通过母体血浆cfDNA的全基因组启动子核小体谱分析评估胎儿巨大儿的风险是可行的。
方法：在妊娠12+0-27+6周时，共有3600个样本的常规无创产前检测（NIPT）数据，对母体浆细胞游离DNA（cfDNA）进行低覆盖率全基因组测序，是从三家独立医院收集的。通过比较巨大儿病例和对照之间的启动子概况，我们确定了具有显着差异覆盖的一组基因。我们选择基因来开发非侵入性预测的分类器，通过使用支持向量机(SVM)和逻辑回归模型，分别。通过曲线下面积(AUC)分析评价每个分类器的性能。
结果：根据现有的随访结果，包括162例巨大胎儿妊娠和648例匹配的对照。在患有巨大儿的妊娠和对照组之间发现了总共1086个具有显著差异的启动子谱的基因(p<0.05)。以AUC为参考，基于SVM(CMA-A2)的分类器性能最好，AUC为0.8256（95%CI：0.7927-0.8586）。
结论：我们的研究表明，基于低覆盖下一代测序，通过母体血浆cfDNA的全基因组启动子核小体谱分析评估胎儿巨大儿的风险是可行的。
