Placental insufficiency affects five to ten% of pregnancies worldwide, and folate deficiency has emerged as a key contributing factor. The molecular pathways linking folate metabolism to placental pathology remain poorly characterized. We developed a heterogeneous graph neural network framework that integrates genomic, transcriptomic, proteomic, and metabolomic data to investigate these mechanisms. Our network architecture explicitly models diverse node types and edge relationships within biological networks, addressing limitations of conventional approaches that treat molecular entities uniformly. The constructed network encompasses 6,704 molecular entities connected through 16,608 validated interactions. Our model achieved 94.7% classification accuracy and 0.978 AUROC, substantially outperforming traditional machine learning methods and single-omics analyses. Attention mechanism analysis identified key molecular signatures including MTHFR downregulation (2.8-fold), FOLR1 depletion (4.5-fold), and homocysteine accumulation (6.3-fold). We identified seven interconnected functional modules spanning folate metabolism, methylation regulation, oxidative stress, and angiogenesis pathways. We acknowledge that the current model was trained on placental tissues collected at delivery, which precludes direct application for antenatal risk prediction. Future studies correlating prenatal biospecimens with our identified placental signatures may enable development of early screening tools. This framework provides a foundation for multiomics integration applicable to diverse pregnancy complications.
Xie et al. (Wed,) studied this question.