Evidence map›Paper›PMID 40660214›Full record

ArticleJournal of translational medicine2025

Molecular subtype of recurrent implantation failure reveals distinct endometrial etiology of female infertility.

Jing Yang, Lingtao Yang, Ying Zhou, Fengyang Cao, Hongkun Fang, Huan Ma, Jun Ren, Chunyu Huang, Lianghui Diao, Qiyuan Li and 1 more

Abstract read
In one paragraph

Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

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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

11 authors.

Jing Yang *Department of Obstetrics and Gynecology, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361102, Fujian, China.
Lingtao Yang *Shenzhen Key Laboratory of Reproductive Immunology for Preimplantation, Shenzhen Zhongshan Institute for Reproductive Medicine and Genetics, Shenzhen Zhongshan Obstetrics & Gynecology Hospital (formerly Shenzhen Zhongshan Urology Hospital), Shenzhen, 518045, Guangdong, China.
Ying Zhou *National Institute for Data Science in Health and Medicine, School of Medicine, Xiamen University, Xiamen, 361102, Fujian, China.
Fengyang CaoNational Institute for Data Science in Health and Medicine, School of Medicine, Xiamen University, Xiamen, 361102, Fujian, China.
Hongkun FangDepartment of Scientific Research Management, Weifang People's Hospital, Shandong Second Medical University, Weifang, 261041, Shandong Province, China.
Huan MaSchool of Medicine, The Chinese University of Hong Kong, 7, Lead contact, Shenzhen, 518172, Guangdong, China.
Jun RenNational Institute for Data Science in Health and Medicine, School of Medicine, Xiamen University, Xiamen, 361102, Fujian, China.
Chunyu HuangShenzhen Key Laboratory of Reproductive Immunology for Preimplantation, Shenzhen Zhongshan Institute for Reproductive Medicine and Genetics, Shenzhen Zhongshan Obstetrics & Gynecology Hospital (formerly Shenzhen Zhongshan Urology Hospital), Shenzhen, 518045, Guangdong, China.
Lianghui DiaoShenzhen Key Laboratory of Reproductive Immunology for Preimplantation, Shenzhen Zhongshan Institute for Reproductive Medicine and Genetics, Shenzhen Zhongshan Obstetrics & Gynecology Hospital (formerly Shenzhen Zhongshan Urology Hospital), Shenzhen, 518045, Guangdong, China. L.diao@pku.edu.cn.ORCID 0000-0002-1159-9261
Qiyuan LiDepartment of Obstetrics and Gynecology, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361102, Fujian, China. qiyuan.li@xmu.edu.cn.ORCID 0000-0002-8934-8948
Qionghua ChenDepartment of Obstetrics and Gynecology, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361102, Fujian, China. cqhua616@126.com.ORCID 0000-0003-4637-9813

Funding

Key Technologies Research and Development Program 2022YFA1303200Key Technologies Research and Development Program 2022YFA1303201National Natural Science Foundation of China 82272944National Natural Science Foundation of China 82371684
6 · The paper itself

Abstract

backgroundRecurrent implantation failure (RIF) remains a significant barrier in assisted reproductive technology (ART), where multiple transfers of high-quality embryos fail to achieve pregnancy. While embryo-related factors have been extensively investigated, the contribution of endometrial dysfunction to RIF remains poorly characterized. This study aimed to determine whether biologically distinct molecular subtypes of endometrial dysfunction exist in RIF and whether such subtypes could guide more personalized and effective treatment strategies.

methodsWe conducted a comprehensive computational analysis integrating publicly available endometrial transcriptomic datasets with prospectively collected samples. Multi-platform data were harmonized using a random-effects model. Differentially expressed genes (DEGs) between RIF and normal samples were identified using MetaDE. Clinical and hormonal correlations were used to assess heterogeneity among RIF samples. Unsupervised clustering (ConsensusClusterPlus) identified RIF subtypes, and their biological characteristics were analyzed using Gene Set Enrichment Analysis (GSEA). Immunohistochemistry (IHC) was used to evaluate the protein-level expression of selected subtype-associated genes. A molecular classifier (MetaRIF) was developed using the optimal F-score from 64 combinations of machine learning algorithms. Candidate therapeutic compounds were predicted using the Connectivity Map (CMap) database.

resultsA total of 1,776 robust DEGs were identified between RIF and normal samples. Clustering analysis revealed two reproducible RIF subtypes: an immune-driven subtype (RIF-I) and a metabolic-driven subtype (RIF-M). RIF-I was enriched for immune and inflammatory pathways (e.g., IL-17 and TNF signaling, p < 0.01) and showed increased infiltration of effector immune cells. RIF-M was characterized by dysregulation of oxidative phosphorylation, fatty acid metabolism, steroid hormone biosynthesis, and altered expression of the circadian clock gene PER1. Immunohistochemical analysis showed that the T-bet/GATA3 expression ratio mirrored the expected subtype distribution, with higher values in RIF-I and lower values in RIF-M. The MetaRIF classifier accurately distinguished subtypes in independent validation cohorts (AUC: 0.94 and 0.85) and outperformed previously published models (AUC: MetaRIF = 0.88; koot_sig = 0.48; Wang_sig = 0.54; OSR_score = 0.72). CMap-based drug predictions identified sirolimus as a candidate for RIF-I and prostaglandins for RIF-M.

conclusionsOur findings reveal two biologically distinct endometrial subtypes of RIF, highlighting the heterogeneous nature of its pathogenesis. By addressing immune and metabolic dysregulation through subtype-specific approaches, our findings provide a foundation for improving diagnosis and tailoring treatment in RIF, potentially enhancing implantation outcomes in ART.

Indexed as

Embryo ImplantationEndometriumInfertility, FemaleCluster AnalysisClustering AlgorithmsFemaleGene Expression ProfilingGene Expression RegulationHumansTranscriptomeTreatment FailureEndometrial dysfunctionImmune activationInflammationMetabolic disordersPersonalized medicineRecurrent implantation failureTranscriptomics

Identifiers

PMID40660214
PMCPMC12257665

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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.