Multi-omics biomarkers in female fertility: from oocyte quality to endometrial receptivity and clinical translation

Abstract High-throughput omics technologies have transformed our understanding of female reproductive biology, revealing molecular interactions governing oocyte maturation, ovarian reserve, endometrial receptivity, and embryo development, yet most findings remain fragmented and insufficiently translated into clinical practice. This review synthesizes multi-omics biomarker discoveries across the reproductive continuum, organizing evidence around three clinically accessible compartments: the follicular unit (follicular fluid, granulosa/cumulus cells), the endometrium (endometrial tissue, uterine fluid), and the embryo (spent culture media). For each compartment, we first characterize single-layer signatures: transcriptomic markers of oocyte developmental competence, metabolic profiles reflecting follicular health, immune and decidualization programs defining endometrial receptivity, and embryo secretomic footprints. We then examine how integrative analytical frameworks and computational modeling enhance prediction accuracy beyond individual modalities, enable mechanistic patient stratification, and reveal coordination failures invisible to single-omics approaches. Critical evaluation of receptivity testing illustrates that distinguishing timing misalignment from intrinsic endometrial dysfunction requires multi-layer endotyping beyond transcriptomics alone, while embryo selection benefits from combining metabolic efficiency readouts with morphokinetic parameters and stress response signatures. The review addresses translational challenges including workflow constraints, lack of standardization, limited external validation, and cost-effectiveness issues, positioning multi-omics biomarkers within clinically defensible scenarios, such as recurrent implantation failure with euploid embryos and embryo selection under discordant morphological signals, where integration may add actionable value. Future directions include precision-guided treatment strategies, proactive fertility monitoring, and AI-driven decision support, with multi-omics biomarkers representing the molecular foundation for transforming reproductive medicine from reactive infertility treatment to proactive fertility management.