Advances in endometrial receptivity and embryo implantation by multi-omics techniques

Endometrial receptivity (ER) is a critical determinant of successful embryo implantation, yet current clinical assessments (e.g., ultrasound, hysteroscopy) primarily focus on morphological evaluation and lack molecular-level insights. Abnormal ER contributes to infertility, recurrent implantation failure (RIF), and miscarriage, necessitating advanced tools to decipher its complex mechanisms. This review explores the application of multi-omics technologies—transcriptomics, proteomics, and metabolomics—to comprehensively analyze ER dynamics, aiming to identify biomarkers and improve assisted reproductive outcomes. Transcriptomics has revealed key genes (e.g., LIF, HOXA10, ITGB3) and non-coding RNAs (e.g., lncRNA H19, miR-let-7) regulating embryo adhesion and immune tolerance. The Endometrial Receptivity Array (ERA), based on 238 coding genes, exemplifies clinical translation but overlooks non-coding RNA contributions. Proteomics studies, utilizing LC-MS and iTRAQ, identified proteins like HMGB1 and ACSL4 linked to ER, while metabolomics highlighted metabolic shifts (e.g., arachidonic acid pathways) in the secretory-phase endometrium. Single-cell and spatial multi-omics further resolved cellular heterogeneity and localized molecular interactions, such as lncRNA H19 enrichment in endometrial stroma. Results demonstrate that integrating multi-omics data enhances predictive accuracy (e.g., machine learning models achieving AUC >0.9) and enables non-invasive diagnostics via uterine fluid or exosomal biomarkers. However, challenges persist, including technical standardization, dynamic monitoring, and clinical validation. In conclusion, multi-omics approaches transform ER assessment from static markers to dynamic network analyses, offering personalized strategies for infertility management. Future advancements in single-cell resolution, AI-driven models, and cross-species validation will bridge research and clinical practice, ultimately optimizing pregnancy success rates.