ArticleJournal of cellular and molecular medicine2024
FANCD2 as a ferroptosis-related target for recurrent implantation failure by integrated bioinformatics and Mendelian randomization analysis.
Article in Journal of cellular and molecular medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- Research progress of ferroptosis in gynecological diseases.Annals of medicine · 2026Review
- Ultrarare Variants in DNA Damage Repair and Mitochondrial Genes in Pediatric Acute-Onset Neuropsychiatric Syndrome and Acute Behavioral Regression in Neurodevelopmental Disorders.Developmental neuroscience · 2026Article
- Machine Learning Identification of Neutrophil Extracellular Trap-Related Genes as Potential Biomarkers and Therapeutic Targets for Bronchopulmonary Dysplasia.International journal of molecular sciences · 2025Article
- FANCD2 as a ferroptosis-related target for recurrent implantation failure by integrated bioinformatics and Mendelian randomization analysis.Journal of cellular and molecular medicine · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Despite advancements in assisted reproductive technology, recurrent implantation failure (RIF) remains a challenge. Endometrial factors, including ferroptosis and immunity, may contribute to this issue. This study integrated bioinformatics analysis and Mendelian randomization (MR) to investigate the expression and significance of DEFRGs in RIF. We intersected 484 ferroptosis-associated genes with 515 differentially expressed genes (DEGs) to identify key DEFRGs. Subsequent analyses included enrichment analysis, molecular subtype identification, machine learning model development for biomarker discovery, immune cell infiltration assessment, single-cell RNA sequencing, and MR to explore the causal relationships of selected genes with RIF. In this study, we identified 11 differentially expressed ferroptosis-related genes (DEFRGs) between RIF and healthy individuals. Cluster analysis revealed two distinct molecular subtypes with different immune profiles and DEFRG expressions. Machine learning models highlighted MUC1, GJA1 and FANCD2 as potential diagnostic biomarkers, with high accuracy in RIF prediction. Single-cell analysis further revealed the cellular localization and interactions of DEFRGs. MR suggested a protective effect of FANCD2 against RIF. Validation in RIF patients confirmed the differential expression of key DEFRGs, consistent with bioinformatics findings. This comprehensive study emphasize the significant role of DEFRGs in the pathogenesis of RIF, suggesting that modulating these genes could offer new avenues for treatment. The FANCD2 is a potential gene contributing to RIF pathogenesis through a non-classical ferroptosis-dependent pathway, providing a foundation for personalized therapeutic strategies in RIF management.
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Registered trials
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