Comparing the Predictivity of Human Placental Gene, microRNA, and CpG Methylation Signatures in Relation to Perinatal Outcomes

Toxicol Sci. 2021 Sep 28;183(2):269-284. doi: 10.1093/toxsci/kfab089.


Molecular signatures are being increasingly integrated into predictive biology applications. However, there are limited studies comparing the overall predictivity of transcriptomic versus epigenomic signatures in relation to perinatal outcomes. This study set out to evaluate mRNA and microRNA (miRNA) expression and cytosine-guanine dinucleotide (CpG) methylation signatures in human placental tissues and relate these to perinatal outcomes known to influence maternal/fetal health; namely, birth weight, placenta weight, placental damage, and placental inflammation. The following hypotheses were tested: (1) different molecular signatures will demonstrate varying levels of predictivity towards perinatal outcomes, and (2) these signatures will show disruptions from an example exposure (ie, cadmium) known to elicit perinatal toxicity. Multi-omic placental profiles from 390 infants in the Extremely Low Gestational Age Newborns cohort were used to develop molecular signatures that predict each perinatal outcome. Epigenomic signatures (ie, miRNA and CpG methylation) consistently demonstrated the highest levels of predictivity, with model performance metrics including R2 (predicted vs observed) values of 0.36-0.57 for continuous outcomes and balanced accuracy values of 0.49-0.77 for categorical outcomes. Top-ranking predictors included miRNAs involved in injury and inflammation. To demonstrate the utility of these predictive signatures in screening of potentially harmful exogenous insults, top-ranking miRNA predictors were analyzed in a separate pregnancy cohort and related to cadmium. Key predictive miRNAs demonstrated altered expression in association with cadmium exposure, including miR-210, known to impact placental cell growth, blood vessel development, and fetal weight. These findings inform future predictive biology applications, where additional benefit will be gained by including epigenetic markers.

Keywords: computational toxicology; epigenomics; machine learning; multi-omics; placenta; predictive biology.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • DNA Methylation
  • Dinucleoside Phosphates / metabolism
  • Female
  • Humans
  • Infant, Newborn
  • Methylation
  • MicroRNAs* / genetics
  • MicroRNAs* / metabolism
  • Placenta / metabolism
  • Pregnancy


  • Dinucleoside Phosphates
  • MicroRNAs
  • cytidylyl-3'-5'-guanosine