Identification of disulfidptosis-related genes and subgroups in Alzheimer’s disease

作者：Shijia Ma, Dan Wang, Daojun Xie · 发表于：Frontiers in Aging Neuroscience · 年份：2023 · DOI：10.3389/fnagi.2023.1236490 · 被引用次数：51 · 研究领域：Ferroptosis and cancer prognosis、Alzheimer's disease research and treatments、Bioinformatics and Genomic Networks

Background Alzheimer’s disease (AD), a common neurological disorder, has no effective treatment due to its complex pathogenesis. Disulfidptosis, a newly discovered type of cell death, seems to be closely related to the occurrence of various diseases. In this study, through bioinformatics analysis, the expression and function of disulfidptosis-related genes (DRGs) in Alzheimer’s disease were explored. Methods Differential analysis was performed on the gene expression matrix of AD, and the intersection of differentially expressed genes and disulfidptosis-related genes in AD was obtained. Hub genes were further screened using multiple machine learning methods, and a predictive model was constructed. Finally, 97 AD samples were divided into two subgroups based on hub genes. Results In this study, a total of 22 overlapping genes were identified, and 7 hub genes were further obtained through machine learning, including MYH9, IQGAP1, ACTN4, DSTN, ACTB, MYL6, and GYS1. Furthermore, the diagnostic capability was validated using external datasets and clinical samples. Based on these genes, a predictive model was constructed, with a large area under the curve (AUC = 0.8847), and the AUCs of the two external validation datasets were also higher than 0.7, indicating the high accuracy of the predictive model. Using unsupervised clustering based on hub genes, 97 AD samples were divided into Cluster1 ( n = 24) and Cluster2 ( n = 73), with most hub genes expressed at higher levels in Cluster2...