Evidence map›Paper›PMID 39779839›Full record

ArticleScientific reports2025

Identifying preeclampsia-associated key module and hub genes via weighted gene co-expression network analysis.

Jie Li, Lingling Jiang, Haili Kai, Yang Zhou, Jiachen Cao, Weichun Tang

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Jie LiDepartment of Operating Room Nursing Group, Affiliated Hospital 2 of Nantong University, Nantong, Jiangsu, China.
Lingling JiangDepartment of Gynaecology and Obstetrics, Affiliated Hospital 2 of Nantong University, Nantong, Jiangsu, China.
Haili KaiDepartment of Gynaecology and Obstetrics, Affiliated Hospital 2 of Nantong University, Nantong, Jiangsu, China.
Yang ZhouDepartment of Gynaecology and Obstetrics, Affiliated Hospital 2 of Nantong University, Nantong, Jiangsu, China.
Jiachen CaoDepartment of Gynaecology and Obstetrics, Affiliated Hospital 2 of Nantong University, Nantong, Jiangsu, China.
Weichun TangDepartment of Gynaecology and Obstetrics, Affiliated Hospital 2 of Nantong University, Nantong, Jiangsu, China. ntyytwc@163.com.

Funding

Clinical Medicine Project of Nantong University Scientific Research Fund 2022JY006Jiangsu Provincial Health Commission Maternal and Child Health Research Project F202110Research Fund Project of Nantong Health Commission MS2023040Research on population development in Nantong City 20231114
6 · The paper itself

Abstract

Preeclampsia (PE) is a common hypertensive disease in women with pregnancy. With the development of bioinformatics, WGCNA was used to explore specific biomarkers to provide therapy targets efficiently. All samples were obtained from gene expression omnibus (GEO), then we used a package named "WGCNA" to construct a scale-free co-expression network and modules related to PE. Next, the search tool for the retrieval of interacting genes database (STRING) was adopted to structure the protein-protein interaction (PPI) of genes in the hub module. Furthermore, the MCODE plug-in was applied to discern hub clusters of the PPI network. We also utilized clusterprofiler to execute the functional analysis. Finally, hub genes were selected via Venn Plot and confirmed by quantitative real-time polymerase chain reaction. Through the co-expression networks and modules, we ensured the turquoise module was the most significant one related to PE. Functional analysis implied these genes were mainly enriched in the organic hydroxy compound metabolic process and Phosphatidylinositol signal system. Due to connectivity, the PPI network showed that GAPDH and VEGFA were the most conspicuous. Lastly, the Venn Plot screened eight hub genes (LDHA, ENG, OCRL, PIK3CB, FLT1, HK2, PKM, and LEP). LDHA was confirmed to be downregulated in PE tissues (P<0.001). This study revealed vital module and hub genes associated with preeclampsia and indicated that LDHA might be a therapeutic target in the future.

Indexed as

Computational BiologyGene Expression ProfilingGene Regulatory NetworksPre-EclampsiaProtein Interaction MapsDatabases, GeneticFemaleGene Expression RegulationHumansPregnancyHub genesModulePreeclampsiaWGCNA

Identifiers

PMID39779839
PMCPMC11711461

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