ArticleInternational journal of molecular sciences2023
Recurrent Implantation Failure: Bioinformatic Discovery of Biomarkers and Identification of Metabolic Subtypes.
Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
The trial behind it
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Who cites it
6 citing papers in PubMed.
- Machine learning-based endometrial ultrasound radiomics habitat analysis for predicting pregnancy outcomes after embryo transfer.Journal of ovarian research · 2026Article
- Multidimensional regulatory architecture of endometrial receptivity and its translational implications for implantation failure.Frontiers in endocrinology · 2026Review
- Molecular subtype of recurrent implantation failure reveals distinct endometrial etiology of female infertility.Journal of translational medicine · 2025Article
- Shared diagnostic biomarkers and underlying mechanisms between endometriosis and recurrent implantation failure.Frontiers in endocrinology · 2025Article
- Personalized embryo transfer guided by rsERT improves pregnancy outcomes in patients with repeated implantation failure.Frontiers in medicine · 2024Article
- Fatty Acid Metabolism Disruptions: A Subtle yet Critical Factor in Adverse Pregnancy Outcomes.International journal of biological sciences · 2024Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recurrent implantation failure (RIF) is a challenging scenario from different standpoints. This study aimed to investigate its correlation with the endometrial metabolic characteristics. Transcriptomics data of 70 RIF and 99 normal endometrium tissues were retrieved from the Gene Expression Omnibus database. Common differentially expressed metabolism-related genes were extracted and various enrichment analyses were applied. Then, RIF was classified using a consensus clustering approach. Three machine learning methods were employed for screening key genes, and they were validated through the RT-qPCR experiment in the endometrium of 10 RIF and 10 healthy individuals. Receiver operator characteristic (ROC) curves were generated and validated by 20 RIF and 20 healthy individuals from Peking University People's Hospital. We uncovered 109 RIF-related metabolic genes and proposed a novel two-subtype RIF classification according to their metabolic features. Eight characteristic genes (
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Registered trials
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