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Screening for Non-Invasive rsRNA Biomarkers to Assess Embryo Quality Using Ultra-Sensitive Pandora Sequencing Combined with Machine Learning

ABSTRACT: Accurate embryo selection is vital for successful in vitro fertilization (IVF), but current morphological scoring methods are somewhat subjective and do not reflect molecular changes. This study employs ultra-sensitive Pandora sequencing to detect highly modified rsRNAs in culture media, aiming to identify molecular markers for non-invasive embryo quality assessment. Machine learning identified four candidate rsRNAs (5S, 5.8S, 28-1S, 28-2S) associated with embryo quality, with cross-validation demonstrating high predictive accuracy (AUC = 0.955). Quantitative RT-PCR further confirmed that 5.8S and 28-2S levels were significantly higher in the culture media of high-quality embryos. These findings suggest that specific rsRNAs could serve as non-invasive markers for embryo selection, offering new insights into rsRNA functions in embryo development.

ORGANISM(S): Homo sapiens

PROVIDER: GSE278434 | GEO | 2025/12/09

REPOSITORIES: GEO

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