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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Immunological Tolerance and Regulation
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1416297

Development and Validation of Preeclampsia Predictive Models Using Key Genes from Bioinformatics and Machine Learning Approaches

Provisionally accepted
Qian Li Qian Li 1Xiaowei Wei Xiaowei Wei 1Fan Wu Fan Wu 2Chuanmei Qin Chuanmei Qin 1Junpeng Dong Junpeng Dong 1Cailian Chen Cailian Chen 3Yi Lin Yi Lin 1*
  • 1 Shanghai Sixth People's Hospital, Shanghai Jiao Tong University, Shanghai, China
  • 2 International Peace Maternity and Child Health Hospital, Shanghai, Shanghai Municipality, China
  • 3 Department of Automation, Shanghai Jiao Tong University, Shanghai, Shanghai Municipality, China

The final, formatted version of the article will be published soon.

    Background: Preeclampsia (PE) poses significant diagnostic and therapeutic challenges. This study aims to identify novel genes for potential diagnostic and therapeutic targets, illuminating the immune mechanisms involved. Methods: Three GEO datasets were analyzed, merging two for training set, and using the third for external validation. Intersection analysis of differentially expressed genes (DEGs) and WGCNA highlighted candidate genes. These were further refined through LASSO, SVM-RFE, and RF algorithms to identify diagnostic hub genes. Diagnostic efficacy was assessed using ROC curves. A predictive nomogram and fully Connected Neural Network (FCNN) were developed for PE prediction. ssGSEA and correlation analysis were employed to investigate the immune landscape. Further validation was provided by qRT-PCR on human placental samples. Result: Five biomarkers were identified with validation AUCs: CGB5 (AUC=0.663, 95%CI: 0.577-0.750), LEP (AUC=0.850, 95%CI: 0.792-0.908), LRRC1 (AUC=0.797, 95%CI: 0.728-0.867), PAPPA2 (AUC=0.839, 95%CI:0.775-0.902), and SLC20A1 (AUC=0.811, 95%CI: 0.742-0.880) also involved in crucial biological processes. The predictive nomogram exhibited significant capability (C-index 0.889), while FCNN achieved a best AUC of 0.927 (95% CI: 0.7782-1.000). Immune infiltration analysis underscored the critical role of T cell subsets, neutrophils, and natural killer cells in PE, with these hub genes closely linked to immune infiltration and the immunological mechanisms of PE's pathogenesis. Conclusion: CGB5, LEP, LRRC1, PAPPA2, and SLC20A1 are validated as key diagnostic biomarkers for PE. Nomogram and FCNN could credibly predict PE. Their association with immune infiltration underscores the crucial role of immune responses in PE pathogenesis.

    Keywords: Preeclampsia, machine learning, deep learning, bioinformatics, Immune Cell Infiltration

    Received: 09 Jul 2024; Accepted: 27 Sep 2024.

    Copyright: © 2024 Li, Wei, Wu, Qin, Dong, Chen and Lin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Yi Lin, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University, Shanghai, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.