Evidence map›Paper›PMID 41034826›Full record

ArticleBMC pregnancy and childbirth2025

Transcriptomics-based identification of lysine crotonylation-related biomarkers in pre-eclampsia.

Junbo Li, Xinyun Li, Xi Chen, Yanyan Guo, Fang Yang

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Junbo LiDepartment of Fetal Medicine and Prenatal Diagnosis, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Xinyun LiDepartment of Fetal Medicine and Prenatal Diagnosis, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Xi ChenDepartment of Fetal Medicine and Prenatal Diagnosis, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Yanyan GuoDepartment of Obstetrics, Renmin Hospital of Wuhan University, Wuhan, China.
Fang YangDepartment of Fetal Medicine and Prenatal Diagnosis, Zhujiang Hospital, Southern Medical University, Guangzhou, China. 964175870@qq.com.

Funding

Natural Science Foundation of Guangdong Province 2022A1515010359
6 · The paper itself

Abstract

backgroundLysine crotonylation has been implicated in the pathogenesis of preeclampsia (PE). However, the underlying mechanisms remain unclear. This study aimed to identify crotonylation-related biomarkers in PE through bioinformatics analysis.

methodsPublicly available datasets, including GSE48424 and GSE149440, were utilized in this study. Candidate biomarkers were identified by intersecting differentially expressed genes (DEGs) from differential expression analysis with key module genes obtained through weighted gene co-expression network analysis (WGCNA). Biomarkers were further refined using expression analysis and machine learning techniques. Functional analysis, immune infiltration analysis, methylation modifications, and construction of molecular regulatory networks were employed to explore the potential mechanisms underlying the involvement of candidate biomarkers in PE pathogenesis. Additionally, associations between the biomarkers and common clinical predictive molecules were examined, and their diagnostic performance for PE was evaluated.

resultsA total of 323 candidate genes were identified by intersecting 3,353 DEGs with 402 key module genes. Expression analysis and machine learning algorithms pinpointed DPYD and PRDX3 as biomarkers. DPYD and PRDX3 were identified as lysine crotonylation-related biomarkers in PE. These genes were significantly downregulated in PE and associated with pathways such as olfactory signaling, neutrophil degranulation, and immune microenvironment dysregulation. Both biomarkers demonstrated excellent diagnostic efficacy (AUC > 0.85), outperforming traditional markers like VEGFA. Additionally, DPYD and PRDX3 are associated with oxidative stress and immune dysregulation, which may be the key mechanisms for the development of PE.

conclusionThis study identified DPYD and PRDX3 as lysine crotonylation-related biomarkers in PE, providing new insights for the diagnosis and management of the condition.

Indexed as

LysinePre-EclampsiaBiomarkersComputational BiologyFemaleGene Expression ProfilingGene Regulatory NetworksHumansMachine LearningPregnancyTranscriptomeBiomarkersLysineBioinformaticsDPYDLysine crotonylationPRDX3Pre-eclampsiaTranscriptomics

Identifiers

PMID41034826
PMCPMC12490086

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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.