Identification and validation of potential diagnostic signature and immune cell infiltration for NAFLD based on cuproptosis-related genes by bioinformatics analysis and machine learning

作者：Guoqing Ouyang, Zhan Wu, Zhipeng Liu, Guandong Pan, Yong Wang, Jing Liu, Jixu Guo, Tao Liu, Guozhen Huang, Yonglian Zeng, Zaiwa Wei, Songqing He, Guandou Yuan · 发表于：Frontiers in Immunology · 年份：2023 · DOI：10.3389/fimmu.2023.1251750 · 被引用次数：26 · 研究领域：Liver Disease Diagnosis and Treatment、Ferroptosis and cancer prognosis、Lipid metabolism and disorders

Background and aims: Cuproptosis has been identified as a key player in the development of several diseases. In this study, we investigate the potential role of cuproptosis-related genes in the pathogenesis of nonalcoholic fatty liver disease (NAFLD). Method: The gene expression profiles of NAFLD were obtained from the Gene Expression Omnibus database. Differential expression of cuproptosis-related genes (CRGs) were determined between NAFLD and normal tissues. Protein-protein interaction, correlation, and function enrichment analyses were performed. Machine learning was used to identify hub genes. Immune infiltration was analyzed in both NAFLD patients and controls. Quantitative real-time PCR was employed to validate the expression of hub genes. Results: Four datasets containing 115 NAFLD and 106 control samples were included for bioinformatics analysis. Three hub CRGs (NFE2L2, DLD, and POLD1) were identified through the intersection of three machine learning algorithms. The receiver operating characteristic curve was plotted based on these three marker genes, and the area under the curve (AUC) value was 0.704. In the external GSE135251 dataset, the AUC value of the three key genes was as high as 0.970. Further nomogram, decision curve, calibration curve analyses also confirmed the diagnostic predictive efficacy. Gene set enrichment analysis and gene set variation analysis showed these three marker genes involved in multiple pathways that are related to the progression of NAF...