Evidence map›Paper›PMID 40016711›Full record

ArticleBMC pregnancy and childbirth2025

Bioinformatics analysis of shared biomarkers and immune pathways of preeclampsia and periodontitis.

Fangyi Ruan, Yinan Wang, Xiang Ying, Yadan Liu, Jinghui Xu, Huanqiang Zhao, Yawei Zhu, Ping Wen, Xiaotian Li, Qiongjie Zhou and 1 more

Abstract read
In one paragraph

Article in BMC pregnancy and childbirth, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Fangyi Ruan *Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China.
Yinan Wang *Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China.
Xiang Ying *Department of Prenatal Diagnostic Center, The International Peace Maternity & Child Health Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yadan LiuObstetrics and Gynecology Hospital of Fudan University, Shanghai, China.
Jinghui XuObstetrics and Gynecology Hospital of Fudan University, Shanghai, China.
Huanqiang ZhaoInstitute of Maternal and Child Medicine, Shenzhen Maternity and Child Healthcare Hospital, Shenzhen, Guangdong, China.
Yawei ZhuObstetrics and Gynecology Hospital of Fudan University, Shanghai, China.
Ping WenShenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, Guangdong, China. kjkwenping@smu.edu.cn.
Xiaotian LiShenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, Guangdong, China. xiaotianli555@163.com.
Qiongjie ZhouObstetrics and Gynecology Hospital of Fudan University, Shanghai, China. zhouqiongjie1732@fckyy.org.cn.
Hefeng HuangObstetrics and Gynecology Hospital of Fudan University, Shanghai, China. huanghefg@hotmail.com.

Funding

Clinical Research Plan of SHDC SHDC2024CRI077National Key Research and Development Program of China 2021YFC2701600National Key Research and Development Program of China,China 2024YFC2707500National Natural Science Foundation of China,China 82371698Sanming Project of Medicine in Shenzhen No.SZSM202211032Science Foundation of Shanghai 21ZR1410600Shenzhen Key Laboratory of Maternal and Child Health and Diseases ZDSYS20230626091559006Shenzhen Medical Research Special Fund Project C2401035
6 · The paper itself

Abstract

backgroundEpidemiological evidence indicates that preeclampsia (PE) is associated with comorbidities such as periodontitis (PD). However, the underlying mechanism remains unclear. To enhance our understanding of their co-pathogenesis, this research investigated the shared biomarkers and pathological mechanisms.

methodsWe systematically retrieved transcriptomic datasets from the Gene Expression Omnibus database. These datasets encompass a comparative analysis of the periodontium with and without PD and of the placenta with and without PE. Differentially Expressed Genes Analysis and Weighted Gene Go-expression Network Analysis (WGCNA) were used to identify the key crosstalk genes in patients with PD and PE. The functional characterisation of these genes was performed using enrichment analysis. Protein-protein interaction networks and machine learning methods were leveraged to identify shared hub genes. The XG-Boost algorithm was applied to construct diagnostic models to gain insight into disease aetiology. The identified genes were validated by single-cell RNA sequencing to ensure their robustness and biological relevance.

resultsA total of 55 key crosstalk genes were identified, which were primarily enriched in immune-related pathways by using limma and WGCNA. Among them, twenty-four shared hub genes were identified using protein-protein interaction analysis and machine learning methods. The diagnostic model constructed using immune-related genes outperformed the other two models (area under the receiver operating characteristic curve [ROC] = 0.7786 and 0.7454 for PE and PD, respectively). Pathways involving these genes were mapped using the Kyoto Encyclopedia of Genes and Genomes analysis. In addition, single-cell RNA sequencing analysis showed that the expression of BIN2, LYN, PIK3AP1, and NEDD9 in neutrophils was significantly downregulated, and LYN in fibroblasts and endothelial cells was consistently upregulated.

conclusionsShared hub genes and immunologic pathways were identified in PE and PD, characterised by crosstalk between BIN2, LYN, NEDD9, and PIK3AP1, suggesting the pathogenesis of PE and PD, which could pave the way for the development of effective diagnostic, treatment, and management strategies.

Indexed as

PeriodontitisPre-EclampsiaBiomarkersComputational BiologyFemaleGene Expression ProfilingGene Regulatory NetworksHumansMachine LearningPlacentaPregnancyProtein Interaction MapsTranscriptomeBiomarkersBioinformaticsImmune pathwayMachine learningPeriodontitisPreeclampsiaSingle cell

Identifiers

PMID40016711
PMCPMC11866586

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.