Integrated bioinformatics analysis reveals fatty acid metabolism subtypes and immune landscape in recurrent implantation failure

Microarray and RNA-sequencing datasets for recurrent implantation failure (RIF) were retrieved from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were subsequently integrated with lipid metabolism-related gene sets to identify candidate biomarkers. Cross-analysis of these approaches yielded seven candidate genes, which were then subjected to four machine learning algorithms. This led to the identification of five hub genes: PRKAG2, CPT1A, PPARGC1A, PIK3C2G, and PTGS2. Logistic regression further validated these genes as robust biomarkers, enabling the construction of a diagnostic nomogram. Molecular docking using CB-Dock Tools subsequently demonstrated that peucedanin binds favorably to all five hub gene products. Collectively, these findings highlight PRKAG2, CPT1A, PPARGC1A, PIK3C2G, and PTGS2 as promising diagnostic biomarkers for RIF and offer new perspectives for therapeutic intervention.