Evidence map›Paper›PMID 40751792›Full record

ArticleJournal of assisted reproduction and genetics2025

Identification of m5C-related genes and subclusters in recurrent pregnancy loss.

Huanmin Luo, Shuqing Li, Yuming Cao, Jinfeng Xu, Li Wang

Abstract read
In one paragraph

Article in Journal of assisted reproduction and genetics, 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

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. MBD1-Mediated SDHD Methylation Aggravates Preeclampsia Progression via Triggering Mitochondrial Dysfunction.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Huanmin Luo *Department of Obstetrics and Gynecology, Perinatal Medical Center, The Fifth Affiliated Hospital of Sun Yat-sen University, 52 Meihua East Road, Xiangzhou District, Zhuhai, Guangdong, 519000, China.
Shuqing Li *Department of Obstetrics and Gynecology, Perinatal Medical Center, The Fifth Affiliated Hospital of Sun Yat-sen University, 52 Meihua East Road, Xiangzhou District, Zhuhai, Guangdong, 519000, China.
Yuming CaoDepartment of Obstetrics and Gynecology, Perinatal Medical Center, The Fifth Affiliated Hospital of Sun Yat-sen University, 52 Meihua East Road, Xiangzhou District, Zhuhai, Guangdong, 519000, China.
Jinfeng XuDepartment of Obstetrics and Gynecology, Perinatal Medical Center, The Fifth Affiliated Hospital of Sun Yat-sen University, 52 Meihua East Road, Xiangzhou District, Zhuhai, Guangdong, 519000, China.
Li WangDepartment of Obstetrics and Gynecology, Perinatal Medical Center, The Fifth Affiliated Hospital of Sun Yat-sen University, 52 Meihua East Road, Xiangzhou District, Zhuhai, Guangdong, 519000, China. wangli223@mail.sysu.edu.cn.ORCID http://orcid.org/0000-0001-5500-922X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeRecurrent pregnancy loss (RPL), which occurs in 1-5% of couples and nearly half of the cases remain unexplained, is a complex condition influenced by multiple factors. Previous investigations have demonstrated the role of m5C-related genes (MRGs) in cancer prognosis and the significance of epigenetic modifications during pregnancy. However, the connection between MRGs and the pathogenesis of RPL remains elusive. This study endeavors to elucidate this relationship through bioinformatics approaches.

methodsData of 48 endometrial tissue samples were obtained through GEO query. Twenty-one MRGs were analyzed. Multiple machine learning (ML) methods were applied to identify biomarkers. A nomogram was constructed, and further analyses like GSEA and scRNA-seq were carried out using the R software.

resultsFive core biomarkers (DNMT1, SMUG1, ZBTB38, MBD4, and TDG) were pinpointed by ML methods, with the prediction model achieving an AUC of 0.953. Based on hub genes, 24 RPL samples were grouped into cluster A (n = 9) and cluster B (n = 15). The study revealed differences in immune cells and microenvironments, and the scRNA-seq analysis confirmed the connection between immune cells and m5C.

conclusionThis study identified five key m5C-related genes, unraveled their link to immune cells, and developed an accurate RPL diagnostic model. The RPL patients are innovatively divided into two clusters, and the difference of their immune microenvironment is analyzed. This study offers a fresh perspective for examining biomarkers and potential therapeutic targets for RPL.

Indexed as

Abortion, HabitualAdultComputational BiologyEpigenesis, GeneticFemaleHumansMachine LearningNomogramsPregnancy5-Methylcytosine(m5C)Diagnostic biomarkersMachine learning methodsRecurrent pregnancy lossSingle-cell RNA sequencing analysis

Identifiers

PMID40751792
PMCPMC12559506

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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.