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Construction of a pathway-level model for preeclampsia based on gene expression data

Hypertension Research volume 47, pages 2521–2531 (2024)Cite this article

Abstract

Preeclampsia (PE) is a heterogeneous disease that seriously affects the health of mothers and fetuses. Lack of detection assays, its diagnosis and intervention are often delayed when the clinical symptoms are atypical. Using personalized pathway-based analysis and machine learning algorithms, we built a PE diagnosis model consisting of nine core pathways using multiple cohorts from the Gene Expression Omnibus database. The model showed an area under the receiver operating characteristic (AUROC) curve of 0.959 with the data from the placental tissue samples in the development cohort. In the two validation cohorts, the AUROCs were 0.898 and 0.876, respectively. The model also performed well with the maternal plasma data in another validation cohort (AUROC: 0.815). Moreover, we identified tyrosine-protein kinase Lck (LCK) as the hub gene in this model and found that LCK and pLCK proteins were downregulated in placentas from PE patients. The pathway-level model for PE can provide a novel direction to develop molecular diagnostic assay and investigate potential mechanisms of PE in future studies.

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Data availability

The gene expression data used in the present study are publicly available in the GEO database (http://www.ncbi.nlm.nih.gov/geo).

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Acknowledgements

The authors thank all participants in this study, including those who donated placental tissues and those who provided sample data in the GEO database. The graphical abstract was created with BioRender (https://www.biorender.com/). In addition, the authors thank Beijing GAP BioTechnology Co., Ltd. for their assistance with the bioinformatics analysis.

Funding

This work was supported by grants from the Natural Science Foundation of Guangdong Province, China (No. 2023A1515012741), Clinical Frontier Technology Program of the First Affiliated Hospital of Jinan University, China (No. JNU1AF-CFTP-2022-a01209), and Research on the Mechanisms and Clinical Implications of Ferroptosis in Preeclampsia (No. 2023A03J1009). There was no role of the funding body in the study design, data collection and analysis, and manuscript writing.

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  1. These authors contributed equally: Andong He, Ka Cheuk Yip, Daiqiang Lu, Jia Liu

Authors and Affiliations

  1. Department of Obstetrics and Gynecology, Jinan University First Affiliated Hospital, Guangzhou, 510630, China

    Andong He, Ka Cheuk Yip, Jia Liu, Xiufang Wang, Yiling Wei, Ruiling Yan & Ruiman Li

  2. Institute of Molecular and Medical Virology, School of Medicine, Jinan University, Guangzhou, 510632, China

    Daiqiang Lu, Yifeng Liu, Qiao Zhang & Feng Gao

  3. Department of Pathology, The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China

    Zunhao Zhang

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Contributions

Ruiman Li, Feng Gao, Ruiling Yan, Qiao Zhang, Andong He, Daiqiang Lu, and Jia Liu designed the study. Andong He, Ka Cheuk Yip, Daiqiang Lu, Jia Liu, Zunhao Zhang, Xiufang Wang, Yifeng Liu, and Yiling Wei performed the analysis. Andong He and Ka Cheuk Yip wrote the manuscript. Ruiman Li, Feng Gao, Ruiling Yan, and Qiao Zhang assisted in revising the manuscript. All authors read and approved the final manuscript.

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Correspondence to Qiao Zhang, Ruiling Yan, Feng Gao or Ruiman Li.

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He, A., Yip, K.C., Lu, D. et al. Construction of a pathway-level model for preeclampsia based on gene expression data. Hypertens Res 47, 2521–2531 (2024). https://doi.org/10.1038/s41440-024-01753-0

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