Identification and validation of cuproptosis‐related molecular clusters in non‐alcoholic fatty liver disease

作者：Changxu Liu, Changxu Liu, Zhihao Fang, Kai Yang, Yanchao Ji, Xiaoxiao Yu, ZiHao Guo, Zhichao Dong, Tong Zhu, Chang Liu, Chang Liu · 发表于：Journal of Cellular and Molecular Medicine · 年份：2024 · DOI：10.1111/jcmm.18091 · 被引用次数：20 · 研究领域：Liver Disease Diagnosis and Treatment、Endoplasmic Reticulum Stress and Disease、Diet, Metabolism, and Disease

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 cluster...