ArticleFrontiers in molecular neuroscience2023
Identification ferroptosis-related hub genes and diagnostic model in Alzheimer's disease.
Article in Frontiers in molecular neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 16 citations in OpenAlex.
- The SLC7A11 Thermostat: A Molecular Signaling Switch Between Ferroptosis and Disulfidptosis in Neurodegenerative Disease.Molecular neurobiology · 2026Review
- Bioinformatics Analysis of the Diagnostic Value of Copper and Zinc Metabolism-Related Genes in Major Depressive Disorder: An In Silico Multi-Cohort Study.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Article
- Role and mechanisms of ferroptosis in cognitive impairment: From molecular pathways to therapeutic targets (Review).International journal of molecular medicine · 2026Review
- Investigating the Biomarkers for Alzheimer's Disease: Insights from Microarray Analysis, Mendelian Randomization, and Experimental Validation.Current medicinal chemistry · 2026Article
- Article
- Identification of Copper and Iron Metabolism Related Biomarkers in Alzheimer's Disease.Cellular and molecular neurobiology · 2025Article
- Identification of a Four-Gene Signature Based on Metal Metabolism for Alzheimer's Disease Diagnosis.Genes · 2025Article
- Exploring the mechanism of metabolic cell death-related genes AKR1C2 and MAP1LC3A as biomarkers in Parkinson's disease.Scientific reports · 2025Article
- Transcriptomic signatures of oxytosis/ferroptosis are enriched in Alzheimer's disease.BMC biology · 2025Article
- Ferroptosis and Iron Homeostasis: Molecular Mechanisms and Neurodegenerative Disease Implications.Antioxidants (Basel, Switzerland) · 2025Review
- Enhancing nonlinear transcriptome- and proteome-wide association studies via trait imputation with applications to Alzheimer's disease.PLoS genetics · 2025Article
- Bioinformatics and experimental validation identify biomarkers for diagnosing Alzheimer's disease.Frontiers in aging neuroscience · 2025Article
- Bioinformatics-driven exploration of key genes and mechanisms underlying oxidative stress in traumatic brain injury.Frontiers in aging neuroscience · 2025Article
- Identification of Autophagy-Related Biomarkers and Diagnostic Model in Alzheimer's Disease.Genes · 2024Article
- Identification of Blood Biomarkers Related to Energy Metabolism and Construction of Diagnostic Prediction Model Based on Three Independent Alzheimer's Disease Cohorts.Journal of Alzheimer's disease : JAD · 2024Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Ferroptosis is a newly defined form of programmed cell death and plays an important role in Alzheimer's disease (AD) pathology. This study aimed to integrate bioinformatics techniques to explore biomarkers to support the correlation between ferroptosis and AD. In addition, further investigation of ferroptosis-related biomarkers was conducted on the transcriptome characteristics in the asymptomatic AD (AsymAD). Methods: The microarray datasets GSE118553, GSE132903, GSE33000, and GSE157239 on AD were downloaded from the GEO database. The list of ferroptosis-related genes was extracted from the FerrDb website. Differentially expressed genes (DEGs) were identified by R "limma" package and used to screen ferroptosis-related hub genes. The random forest algorithm was used to construct the diagnostic model through hub genes. The immune cell infiltration was also analyzed by CIBERSORTx. The miRNet and DGIdb database were used to identify microRNAs (miRNAs) and drugs which targeting hub genes. Results: We identified 18 ferroptosis-related hub genes anomalously expressed in AD, and consistent expression trends had been observed in both AsymAD The random forest diagnosis model had good prediction results in both training set (AUC = 0.824) and validation set (AUC = 0.734). Immune cell infiltration was analyzed and the results showed that CD4+ T cells resting memory, macrophages M2 and neutrophils were significantly higher in AD. A significant correlation of hub genes with immune infiltration was observed, such as DDIT4 showed strong positive correlation with CD4+ T cells memory resting and AKR1C2 had positive correlation with Macrophages M2. Additionally, the microRNAs (miRNAs) and drugs which targeting hub genes were screened. Conclusion: These results suggest that ferroptosis-related hub genes we screened played a part in the pathological progression of AD. We explored the potential of these genes as diagnostic markers and their relevance to immune cells which will help in understanding the development of AD. Targeting miRNAs and drugs provides new research clues for preventing the development of AD.
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