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Heterogeneous graph neural networks reveal molecular mechanisms of folate deficiency in placental insufficiency through multiomics integration
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  • Published: 11 February 2026

Heterogeneous graph neural networks reveal molecular mechanisms of folate deficiency in placental insufficiency through multiomics integration

  • Xiaohong Xie1 na1,
  • Zhoulan Li1 na1,
  • Qiufeng Xiao1,
  • Huifang Xiong1 &
  • …
  • Mengting Yuan1 

Scientific Reports , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Computational biology and bioinformatics
  • Diseases
  • Genetics

Abstract

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.

Data availability

The multiomics datasets, complete analysis results, and computational code generated during this study are provided in Supplementary Materials. This file contains: (1) processed multiomics data matrices for all 298 samples; (2) complete differential expression results with raw and adjusted p-values for all features (Supplementary Table S1-S3); (3) full feature importance rankings including attention weights, gradient attributions, and permutation importance scores; (4) network construction parameters and edge lists; (5) Python scripts for data preprocessing, graph neural network training, and evaluation using actual experimental data. Patient identifiers have been removed to protect confidentiality. Additional raw sequencing data are available from the corresponding author (Mengting Yuan, ymt1125@outlook.com) upon reasonable request, subject to institutional review board approval and data sharing agreements.

Abbreviations

AUROC:

Area Under the Receiver Operating Characteristic Curve

ChIP-seq:

Chromatin Immunoprecipitation Sequencing

CV:

Coefficient of Variation

DHFR:

Dihydrofolate Reductase

DNA:

Deoxyribonucleic Acid

ELU:

Exponential Linear Unit

FOLR1/FOLR2:

Folate Receptor 1/2

GNN:

Graph Neural Network

HAT:

Heterogeneous Attention

HIF1A:

Hypoxia-Inducible Factor 1 Alpha

HMDB:

Human Metabolome Database

IQR:

Interquartile Range

KEGG:

Kyoto Encyclopedia of Genes and Genomes

LASSO:

Least Absolute Shrinkage and Selection Operator

MTHFR:

Methylenetetrahydrofolate Reductase

MTR:

Methionine Synthase

PCA:

Principal Component Analysis

PCFT/SLC46A1:

Proton-Coupled Folate Transporter

QC:

Quality Control

ReLU:

Rectified Linear Unit

RFC/SLC19A1:

Reduced Folate Carrier

RNA:

Ribonucleic Acid

SHMT:

Serine Hydroxymethyltransferase

SNP:

Single Nucleotide Polymorphism

STAT3:

Signal Transducer and Activator of Transcription 3

TF:

Transcription Factor

VEGF:

Vascular Endothelial Growth Factor

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Acknowledgements

The authors thank the clinical staff at Longyan First Affiliated Hospital of Fujian Medical University for assistance with sample collection and the participants for their invaluable contribution to this research.

Funding

Sponsored by Fujian Province Natural Science Foundation (Grant No. 2024J011622).

Author information

Author notes

  1. These authors contributed equally to this work: Xiaohong Xie and Zhoulan Li.

Authors and Affiliations

  1. Department of Obstetrics, Longyan First Affiliated Hospital of Fujian Medical University, Longyan, 364000, Fujian Province, China

    Xiaohong Xie, Zhoulan Li, Qiufeng Xiao, Huifang Xiong & Mengting Yuan

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Contributions

X.X. and Z.L. contributed equally to this work. X.X. designed the study, coordinated sample collection, performed clinical data analysis, and drafted the initial manuscript. Z.L. conducted multi-omics data preprocessing, quality control procedures, and contributed to experimental validation. Q.X. developed the graph neural network architecture, implemented computational algorithms, and performed bioinformatics analyses. H.X. executed laboratory experiments, managed sample processing, and contributed to data interpretation. M.Y. conceived and supervised the project, provided critical intellectual input, coordinated all research activities, and finalized the manuscript. All authors reviewed, edited, and approved the final manuscript for publication.

Corresponding author

Correspondence to Mengting Yuan.

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The authors declare no competing interests.

Ethics approval and consent to participate

This study was approved by the Research Ethics Committee of Longyan First Affiliated Hospital of Fujian Medical University (Reference Number: IRB-2024-LFAH-067). All participants provided written informed consent prior to enrollment. The study was conducted in accordance with the Declaration of Helsinki and relevant national regulations governing human subject research.

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Xie, X., Li, Z., Xiao, Q. et al. Heterogeneous graph neural networks reveal molecular mechanisms of folate deficiency in placental insufficiency through multiomics integration. Sci Rep (2026). https://doi.org/10.1038/s41598-026-38288-y

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  • Received: 28 October 2025

  • Accepted: 29 January 2026

  • Published: 11 February 2026

  • DOI: https://doi.org/10.1038/s41598-026-38288-y

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Keywords

  • Placental insufficiency
  • Folate metabolism
  • Graph neural networks
  • Multi-omics integration
  • Molecular mechanisms
  • Precision medicine
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