Abstract
Non-alcoholic fatty liver disease (NAFLD) is a major chronic liver disease worldwide. Cuproptosis has recently been reported as a form of cell death that appears to drive the progression of a variety of diseases. This study aimed to explore cuproptosis-related molecular clusters and construct a prediction model. The gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. The associations between molecular clusters of cuproptosis-related genes and immune cell infiltration were investigated using 50 NAFLD samples. Furthermore, cluster-specific differentially expressed genes were identified by the WGCNA algorithm. External datasets were used to verify and screen feature genes, and nomograms, calibration curves and decision curve analysis (DCA) were performed to verify the performance of the prediction model. Finally, a NAFLD-diet mouse model was constructed to further verify the predictive analysis, thus providing new insights into the prediction of NAFLD clusters and risks. The role of cuproptosis in the development of non-alcoholic fatty liver disease and immune cell infiltration was explored. Non-alcoholic fatty liver disease was divided into two cuproptosis-related molecular clusters by unsupervised clustering. Three characteristic genes (ENO3, SLC16A1 and LEPR) were selected by machine learning and external data set validation. In addition, the accuracy of the nomogram, calibration curve and decision curve analysis in predicting NAFLD clusters was also verified. Further animal and cell experiments confirmed the difference in their expression in the NAFLD mouse model and Mouse hepatocyte cell line. The present study explored the relationship between non-alcoholic fatty liver disease and cuproptosis, providing new ideas and targets for individual treatment of the disease.