ArticleScientific reports2024
Unveiling disulfidptosis-related biomarkers and predicting drugs in Alzheimer's disease.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- The SLC7A11 Thermostat: A Molecular Signaling Switch Between Ferroptosis and Disulfidptosis in Neurodegenerative Disease.Molecular neurobiology · 2026Review
- Programmed cell death: a promising management for Alzheimer's disease.Apoptosis : an international journal on programmed cell death · 2026Review
- Metabolic cell death networks in Alzheimer's disease: mechanistic links and therapeutic perspectives of ferroptosis, cuproptosis, and disulfidptosis.Frontiers in cell and developmental biology · 2026Review
- Cynanchum bungei Decne-derived extracellular vesicles alleviate cognitive impairment and pathological damage in Alzheimer's disease.Frontiers in cellular neuroscience · 2026Article
- Construction of a novel 3-gene diagnostic signature related to senescence in intervertebral disc degeneration.European journal of medical research · 2025Article
- Toward precision oncology in LUAD: a prognostic model using single-cell sequencing and WGCNA based on a disulfidptosis relative gene signature.Frontiers in immunology · 2025Article
- Immunometabolic regulation of disulfidptosis in orthopedic diseases: mechanistic heterogeneity and therapeutic targets.Frontiers in immunology · 2025Review
- NDUFA11 may be the disulfidptosis-related biomarker of ischemic stroke based on integrated bioinformatics, clinical samples, and experimental analyses.Frontiers in neuroscience · 2024Article
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Authors and funding
7 authors.
Funding
Abstract
Alzheimer's disease is the predominant form of dementia, and disulfidptosis is the latest reported mode of cell death that impacts various disease processes. This study used bioinformatics to analyze genes associated with disulfidptosis in Alzheimer's disease comprehensively. Based on the public datasets, the differentially expressed genes associated with disulfidptosis were identified, and immune cell infiltration was investigated through correlation analysis. Subsequently, hub genes were determined by a randomforest model. A prediction model was constructed using logistic regression. In addition, the drug-target affinity was predicted by a graph neural network model, and the results were validated by molecular docking. Five hub genes (PPEF1, NEUROD6, VIP, NUPR1, and GEM) were identified. The gene set showed significant enrichment for AD-related pathways. The logistic regression model demonstrated an AUC of 0.952, with AUC values of 0.916 and 0.864 in validated datasets. The immune infiltration analysis revealed significant heterogeneity between the Alzheimer's disease and control groups. High-affinity drugs for hub genes were identified. Through our study, a disease prediction model was constructed using potential biomarkers, and drugs targeting the genes were predicted. These results contribute to further understanding of the molecular mechanisms underlying Alzheimer's disease.
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