Evidence map›Paper›PMID 41325315›Full record

ArticlePloS one2025

Integrated multi-omics analysis reveals necroptosis-related biomarker BIRC3 for early diagnosis and therapeutic targeting in preeclampsia.

Qingxia Lin, Peifeng Huang, Youhong Kang, Yanfeng Lu, Guili Shi

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In one paragraph

Article in PloS one, 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

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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. Review
4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Qingxia LinDepartment of Obstetrics, Quanzhou Women and Children's Hospital, Quanzhou, China.
Peifeng HuangThe Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
Youhong KangDepartment of Obstetrics, Quanzhou Women and Children's Hospital, Quanzhou, China.
Yanfeng LuDepartment of Obstetrics, Quanzhou Women and Children's Hospital, Quanzhou, China.
Guili ShiDepartment of Obstetrics, Quanzhou Women and Children's Hospital, Quanzhou, China.ORCID https://orcid.org/0009-0002-4263-4872

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPreeclampsia (PE) is a life-threatening pregnancy disorder lacking reliable early biomarkers. While apoptosis is implicated in PE pathogenesis, the role of regulated necrotic cell death (necroptosis) remains poorly understood. This study aimed to identify necroptosis-related biomarkers, and further provide the potential natural compounds for PE with virtual screening.

methodsPublic datasets (GSE66273 for training set; GSE44711 for validation set; GSE173193 for single-cell RNA-seq) were analyzed. Differentially expressed genes (DEGs) were screened using limma (|log2FC| > 1, P < 0.05). Necroptosis-related genes overlapped with DEGs to identify key candidates. Subsequent analyses included machine learning, protein-protein interaction (PPI) network construction, and immune infiltration profiling. Single-cell RNA sequencing data (GSE173193) was utilized to localize BIRC3 expression at the cellular level. Transcription factors, microRNAs, and RNA-binding proteins associated with BIRC3 are also identified. Finally, Molecular docking predicted therapeutic drugs targeting hub genes.

resultsThe analysis of the GSE66273 dataset identified 367 DEGs. Intersection with necroptosis-related genes revealed 3 necroptosis-related DEGs (NRDEGs), from which BIRC3 was prioritized as hub gene through PPI networks and machine learning (random forest). BIRC3 demonstrated significant diagnostic potential in the discovery cohort (AUC = 0.933) and maintained strong performance in the independent validation cohort (AUC = 0.844). Single-cell analysis revealed BIRC3 was predominantly expressed in immune lineages, particularly NK/T cells, with a significantly higher proportion of BIRC3-positive cells in PE placentas (p < 0.05). Immune microenvironment further analysis demonstrated significant dysregulation in PE. Finally, based on the BIRC3 structure, Withanolide D (-11.0 kcal/mol), Baicalin (-9.0 kcal/mol) and Mangostin (-8.3 kcal/mol) were screened.

conclusionThis comprehensive analysis implicates necroptosis in PE pathogenesis. BIRC3 is proposed as a novel diagnostic biomarker and therapeutic target, with multi-omics validation underscoring its role in immune dysregulation and placental dysfunction.

Indexed as

Baculoviral IAP Repeat-Containing 3 ProteinNecroptosisPre-EclampsiaBiomarkersEarly DiagnosisFemaleGene Expression ProfilingGene Regulatory NetworksHumansMachine LearningMolecular Docking SimulationMultiomicsPregnancyProtein Interaction MapsSingle-Cell AnalysisBaculoviral IAP Repeat-Containing 3 ProteinBiomarkersBIRC3 protein, human

Identifiers

PMID41325315
PMCPMC12668491

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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 Socratic graph.