Evidence map›Paper›PMID 41331828›Full record

ArticleEuropean journal of medical research2025

Deciphering N7-methylguanosine-driven immune dysregulation in unexplained recurrent spontaneous abortion based on transcriptome data and experimental validation.

Qing Guo, Shimeng Wang, Sujie Song, Xiaoxuan Zhao

Abstract read
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Article in European journal of medical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Qing GuoHeilongjiang Academy of Chinese Medicine Sciences, Harbin, 150080, China.
Shimeng WangDepartment of Gynecology, The First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, 150040, China.
Sujie SongDepartment of Traditional Chinese Medicine (TCM) Gynecology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China.
Xiaoxuan ZhaoDepartment of Traditional Chinese Medicine (TCM) Gynecology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China. tychexuan@163.com.

Funding

Research Project of Zhejiang Chinese Medical University 2024JKZKTS37Zhejiang medical and health project No. 2025KY158Zhejiang Provincial Natural Science Foundation of China LQ24H270019Zhejiang Traditional Medicine and Technology Program, China No. 2025ZR173
6 · The paper itself

Abstract

backgroundUnexplained recurrent spontaneous abortion (URSA) poses significant clinical challenges, with immune dysregulation at the maternal-fetal interface implicated in 80% of cases. While RNA modifications like N7-methylguanosine (m7G) are emerging as key regulators of immune pathologies, their role in URSA remains unexplored.

methodsDecidual transcriptomes (GSE165004) from URSA patients and controls were analyzed to identify differentially expressed m7G regulators. Machine learning (LASSO/random forest) prioritized diagnostic biomarkers, which were validated via qRT-PCR and immunofluorescence. Differences in the immune infiltration landscape between the two groups were assessed by ssGSEA. Furthermore, Spearman correlation analysis was performed to explore associations between m7G-related biomarkers and immune infiltration characteristics. Regulatory networks (transcription factors/microRNAs) and therapeutic candidates of biomarkers were predicted using JASPAR, mirTarbase, and Coremine medical, respectively.

resultsThree key m7G regulators with diagnostic value were identified: LSM1, LARP1, and NCBP2. The nomogram constructed based on these biomarkers demonstrated excellent predictive performance in URSA. qRT-PCR and immunofluorescence confirmed that LSM1 expression was upregulated in URSA samples, while LARP1 and NCBP2 were downregulated. Furthermore, distinct patterns of immune infiltration were observed between URSA and control. Spearman analysis revealed that LSM1 was negatively correlated with Treg infiltration, whereas LARP1 and NCBP2 showed positive correlations with Treg. Besides, network analysis identified regulatory relationships between these biomarkers and 167 miRNAs (e.g., hsa-miR-27a-3p) or 18 TFs (e.g., E2F1, GATA2). Sirolimus (targeting NCBP2) and AZD4547 (targeting LSM1) were predicted to be the most promising therapeutic drugs.

conclusionsThis study establishes m7G methylation as a novel epigenetic driver of immune dysregulation in URSA. The predictive signature offers translational tool for risk stratification and targeted therapy, bridging RNA epigenetics with reproductive immunology.

Indexed as

Abortion, HabitualGuanosineTranscriptomeAdultBiomarkersDeciduaFemaleHumansPregnancy7-methylguanosineBiomarkersGuanosineBiomarkerImmune dysregulationN7-methylguanosineUnexplained recurrent spontaneous abortion

Identifiers

PMID41331828
PMCPMC12771930

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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.