Evidence map›Paper›PMID 41084495›Full record

ArticleFrontiers in genetics2025

Immune-molecular nexus in reproductive disorders: mechanisms linking POI and RSA.

Chen Chen, Xinyue Zhang, Wenxin Li, Yueqin Liu, Dan Zhao, Subo Zhang, Xiaolan Zhu

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Article in Frontiers in genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited 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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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Chen ChenDepartment of Reproductive Medical Center, Fourth Affiliated Hospital of Jiangsu University (Zhenjiang Maternity and Child Healthcare Hospital), Zhenjiang, Jiangsu, China.
Xinyue ZhangDepartment of Reproductive Medical Center, Fourth Affiliated Hospital of Jiangsu University (Zhenjiang Maternity and Child Healthcare Hospital), Zhenjiang, Jiangsu, China.
Wenxin LiDepartment of Radiology, the Second People's Hospital of Lianyungang City, Lianyungang, Jiangsu, China.
Yueqin LiuDepartment of Radiology, the Second People's Hospital of Lianyungang City, Lianyungang, Jiangsu, China.
Dan ZhaoDepartment of Radiology, the Second People's Hospital of Lianyungang City, Lianyungang, Jiangsu, China.
Subo ZhangDepartment of Radiology, the Second People's Hospital of Lianyungang City, Lianyungang, Jiangsu, China.
Xiaolan ZhuDepartment of Reproductive Medical Center, Fourth Affiliated Hospital of Jiangsu University (Zhenjiang Maternity and Child Healthcare Hospital), Zhenjiang, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Infertility remains a prevalent global health concern, with Premature Ovarian Insufficiency (POI) and Recurrent Spontaneous Abortion (RSA) being common causes of female infertility. Objective: This study aims to identify new central genes and potential therapeutic drugs for RSA and POI by integrating multi transcriptome data and machine learning algorithms. Methods: This study utilized RNA sequencing data from patients with POI and RSA to identify key hub genes associated with these diseases. The analysis involved machine learning algorithms, mcode and Cytoscape, revealing important hub genes. The comprehensive evaluation includes functional annotation, protein-protein interaction (PPI) network, transcription factor (TF) gene regulatory network, microRNA (miRNA) gene regulatory network. Genome enrichment analysis (GSEA) and immune infiltration studies elucidated the potential mechanism between POI and RSA. Drug target enrichment analysis highlighted promising therapeutic agents against RSA and POI. Validation of granulosa cells and endometrial tissue samples using quantitative real-time polymerase chain reaction (qRT-PCR) highlighted the importance of the identified hub genes. Results: This study identified a total of six hub genes-- CENPW, ENTPD3, FOXM1, GNAQ, LYPLA1, and PLA2G4A. Immunoassay revealed an increase in activated NK cells. Furthermore, significant differences were observed in the proportions of other immune cell types, such as resting memory CD4 T cells, compared to the control group. Significantly, these six genes participate in diverse metabolic pathways linked to RSA and POI, particularly in oxidative phosphorylation, ribosome processes, and steroid biosynthesis pathways. Additionally, ten potential drugs (Rifabutin, Methaneseleninic Acid, Carbamazepine, Dasatinib,Troglitazone, Tamoxifen, Enterolactone, Anisomycin, Testosterone, 5-Fluorouracil) targeting key genes were identifed. Conclusion: Targeting these genes shows promise for preventing and treating both POI and RSA, providing crucial insights into addressing these complex conditions at molecular level.

Indexed as

drug target enrichmentintegrated transcriptomic analysismachine learningpremature ovarian insufficiencyrecurrent spontaneous abortion

Identifiers

PMID41084495
PMCPMC12515496

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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.