Unraveling shared diagnostic genes and cellular microenvironmental changes in endometriosis and recurrent implantation failure through multi-omics analysis
Abstract Endometriosis and Recurrent Implantation Failure (RIF) are both pivotal clinical issues within the realm of reproductive medicine, sharing significant overlap in their pathophysiological mechanisms. However, research exploring the commonalities between these two conditions remains relativel...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-93146-7 |
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| Summary: | Abstract Endometriosis and Recurrent Implantation Failure (RIF) are both pivotal clinical issues within the realm of reproductive medicine, sharing significant overlap in their pathophysiological mechanisms. However, research exploring the commonalities between these two conditions remains relatively scarce, and reliable shared diagnostic biomarkers have yet to be identified. In this study, we integrated transcriptomic and single-cell sequencing data from the Gene Expression Omnibus (GEO) database to identify shared diagnostic genes and alterations in the cellular microenvironment between EMs and RIF. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify key genes. Machine learning algorithms, including Random Forest (RF) and XGBoost, were utilized to screen for shared diagnostic genes, which were subsequently validated through receiver operating characteristic (ROC) analysis and clinical prediction models. Single-cell analysis was conducted to investigate the expression patterns of these diagnostic genes across various cellular subpopulations. Additionally, gene set enrichment analysis (GSEA) and competing endogenous RNA (ceRNA) network analysis were employed to further elucidate the biological functions and regulatory mechanisms of these genes. A total of 16 key genes were identified, which were predominantly expressed in fibroblasts. Through machine learning, the optimal model combining RF and XGBoost was selected to identify the shared diagnostic genes PDIA4 and PGBD5. Single-cell analysis revealed significant differences in the expression of these diagnostic genes in fibroblasts between normal and disease states. ROC analysis showed that the Area Under the Curve (AUC) values for individual genes in disease diagnosis were all above 0.7. The constructed clinical prediction model demonstrated robust predictive capacity for the disease. Immune infiltration analysis indicated that M2 macrophages and γδ T cells play important roles in the pathogenesis of EMs and RIF. GSEA revealed that these genes are involved in immune responses, vascular function, and hormone regulation, and are regulated by miR-3121-3p. This study provides comprehensive insights into the shared cellular microenvironmental alterations and molecular mechanisms underlying EMs and RIF. The identification of PDIA4 and PGBD5 as shared diagnostic biomarkers offers new avenues for early diagnosis and targeted treatment of EMs-related RIF. Future work will focus on validating these findings in larger cohorts and exploring their therapeutic potential. |
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| ISSN: | 2045-2322 |