Identification and immunological characterization of cuproptosis-related molecular clusters in unexplained infertility

Objective: To investigate the correlation between cuproptosis and unexplained infertility (UI). Methods: Using the GSE165004 dataset, we analyzed the expression profiles and immunological characteristics of cuproptosis regulators in UI. Using 27 cuproptosis-related genes and 24 endometrial samples from patients with UI, we explored molecular clusters based on cuproptosis-related genes and associated immune cell infiltration. Cluster-specific differentially expressed genes were identified using the weighted gene co-expression network analysis algorithm, and differences in pathway and functional enrichment of cuproptosis-related molecular clusters were explored using gene set variation analysis. Subsequently, the performances of seven machine learning models were compared to select the optimal machine model. Nomogram, calibration curve, and decision curve analysis models were used to validate the predictions. Results: Dysregulated and activated immune responses to cuproptosis-related genes were determined between UI patients and healthy female controls of childbearing age. Two cuproptosis-related molecular clusters were defined in UI. Among the machine learning models, the support vector machine (SVM) algorithm had the best discriminative performance, with relatively small residual and root mean square errors and a large area under the curve (0.918). To further evaluate the predictive efficiency of the SVM model, we constructed a nomogram to estimate the risk of aggregation of five cuproptosis-related genes. Calibration curve and decision curve analyses also demonstrated the accuracy of predicting subtypes of UI. These analyses revealed that five cuproptosis-related genes were significantly associated with UI. Conclusion: Our study systematically reveals a complex relationship between cuproptosis and UI, and these cuproptosis-related genes may serve as new candidate prognostic biomarkers for UI.