Recurrent miscarriage (RM) is a reproductive disorder affecting couples worldwide. The underlying molecular mechanisms remain elusive, even though emerging evidence has implicated endoplasmic reticulum stress (ERS). We investigated RM- and ERS-related genes to develop a diagnostic model that can enhance predictive ability. We utilized the R package GEO query to extract and process Gene Expression Omnibus data, applying batch correction, normalization, and differential gene expression analysis with limma. ERS-related differentially expressed genes (ERSRGs) were identified through Gene Ontology and Kyoto Encyclopedia of genes and genomes analyses, and their diagnostic potential was assessed. Diagnostic models were developed using logistic regression, support vector machines, and least absolute shrinkage and selection operators, complemented by immune infiltration analysis and regulatory network construction. Integrated analysis revealed 1395 differentially expressed genes (DEGs), including 626 upregulated and 769 downregulated genes. Seventeen ERSRGs were identified. KEAP1 and YIPF5 displayed high diagnostic accuracy (area under the curve [AUC] > 0.9). Gene Ontology and Kyoto Encyclopedia of genes and genomes analyses highlighted the role of ESRDEGs in cellular responses to ERS, protein processing, and apoptosis. Diagnostic models demonstrated robust predictive performance (AUC > 0.9). A molecular interaction was found between RM and the ERS response, and the identified ESRDEGs could serve as potential biomarkers for diagnosis.
Keywords: Bioinformatics; Diagnostic model; Endoplasmic reticulum stress; Immune Infiltration; Recurrent miscarriage.
© 2025. The Author(s).