Imagine a jigsaw puzzle, where the pieces are scattered all over the room. Now, picture yourself painstakingly gathering the pieces that fit together perfectly. That’s exactly what scientists did in this study, but instead of puzzle pieces, they identified the genes associated with a newly discovered form of cell death called disulfidptosis, and connected them to Alzheimer’s disease (AD). By using advanced bioinformatics techniques, the researchers pinpointed 22 overlapping genes, known as disulfidptosis-related genes (DRGs), in AD patients. These DRGs play a critical role in the complex pathogenesis of AD. To further explore their function, seven hub genes were identified through machine learning methods. With those hub genes, the scientists built a predictive model that accurately assessed the risk of the disulfidptosis subtype in AD patients. The results were impressive, with the model displaying high accuracy and validation across multiple datasets. Additionally, when analyzing immune infiltration levels between patient subgroups, the researchers discovered that higher immune infiltration was linked to more severe AD symptoms. This groundbreaking study not only sheds light on the close association between disulfidptosis and AD, but also opens up new possibilities for biomarker discovery and potential therapeutic strategies. It’s like finding a hidden treasure chest filled with clues to combat this devastating neurological disorder! Want to learn more about this exciting research? Check out the full article!

BackgroundAlzheimer’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.MethodsDifferential 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.ResultsIn 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. Immune infiltration analysis revealed that Cluster2 had a higher level of immune infiltration and immune scores.ConclusionA close association between disulfidptosis and Alzheimer’s disease was discovered in this study, and a predictive model was established to assess the risk of disulfidptosis subtype in AD patients. This study provides new perspectives for exploring biomarkers and potential therapeutic targets for Alzheimer’s disease.

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