Identification of key genes in the pathogenesis of preeclampsia via bioinformatic analysis and experimental verification

Yongqi Gao; Zhongji Wu; Simin Liu; Yiwen Chen; Guojun Zhao; Hui-Ping Lin

doi:10.3389/fendo.2023.1190012

Identification of key genes in the pathogenesis of preeclampsia via bioinformatic analysis and experimental verification

Front Endocrinol (Lausanne). 2023 Jul 28:14:1190012. doi: 10.3389/fendo.2023.1190012. eCollection 2023.

Authors

Yongqi Gao¹, Zhongji Wu¹, Simin Liu¹, Yiwen Chen¹, Guojun Zhao², Hui-Ping Lin¹

Affiliations

¹ Department of Basic Medical Research, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People's Hospital, Key Laboratory of Cardiovascular Diseases, School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, China.
² The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan City People's Hospital, Qingyuan, Guangdong, China.

Abstract

Background: Preeclampsia (PE) is the primary cause of perinatal maternal-fetal mortality and morbidity. The exact molecular mechanisms of PE pathogenesis are largely unknown. This study aims to identify the hub genes in PE and explore their potential molecular regulatory network.

Methods: We downloaded the GSE148241, GSE190971, GSE74341, and GSE114691 datasets for the placenta and performed a differential expression analysis to identify hub genes. We performed Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO), Gene Set Enrichment Analysis (GSEA), and Protein-Protein Interaction (PPI) Analysis to determine functional roles and regulatory networks of differentially expressed genes (DEGs). We then verified the DEGs at transcriptional and translational levels by analyzing the GSE44711 and GSE177049 datasets and our clinical samples, respectively.

Results: We identified 60 DEGs in the discovery phase, consisting of 7 downregulated genes and 53 upregulated genes. We then identified seven hub genes using Cytoscape software. In the verification phase, 4 and 3 of the seven genes exhibited the same variation patterns at the transcriptional level in the GSE44711 and GSE177049 datasets, respectively. Validation of our clinical samples showed that CADM3 has the best discriminative performance for predicting PE.

Conclusion: These findings may enhance the understanding of PE and provide new insight into identifying potential therapeutic targets for PE.

Keywords: bioinformatics; biomarkers; differentially expressed genes; preeclampsia; therapeutic targets.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Computational Biology
Female
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
Gene Regulatory Networks*
Humans
Pre-Eclampsia* / genetics
Pregnancy

Grants and funding

This work was supported by National Natural Science Foundation for Young Scientists of China (No. 82201809), Guangdong Natural Science Foundation (No. 2023A1515010514), and China Postdoctoral Science Foundation fellowship (No. 2022M720902) awarded to HL.