ArticleFrontiers in molecular neuroscience2023
Identification of mitochondrial-related genes as potential biomarkers for the subtyping and prediction of 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 7 papers.
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Who cites it
7 citing papers in PubMed, 7 citations in OpenAlex.
- Role of voltage-dependent anion channel 1 in neurodegeneration: Mechanisms, implications, and therapeutic potential.Neural regeneration research · 2026Article
- Mechanisms and Therapeutic Potential of Targeting Mitophagy in Neurodegenerative Diseases.Molecular neurobiology · 2026Review
- Molecular diversity of mitochondrial autophagy receptors: context-dependent effects in human health and disease.Cell death discovery · 2026Review
- Allele specific expression in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Identification and verification of hub genes related to mitochondrial dysfunction in epilepsy based on bioinformatics analysis.BMC neurology · 2026Article
- Tau-Mitochondria Interactions in Neurodegeneration: Mechanisms and Therapeutic Potential.Cellular and molecular neurobiology · 2025Review
- Review of FUNDC1-mediated mitochondrial autophagy in Alzheimer's disease.Frontiers in aging neuroscience · 2025Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Alzheimer's disease (AD) is a progressive and debilitating neurodegenerative disorder prevalent among older adults. Although AD symptoms can be managed through certain treatments, advancing the understanding of underlying disease mechanisms and developing effective therapies is critical. Methods: In this study, we systematically analyzed transcriptome data from temporal lobes of healthy individuals and patients with AD to investigate the relationship between AD and mitochondrial autophagy. Machine learning algorithms were used to identify six genes- Results: The identified genes are closely linked to the onset and progression of AD and can serve as reliable biomarkers. The differences in gene expression, clinical features, immune infiltration, and pathway enrichment were examined among the three AD subtypes. Potential drugs for the treatment of each subtype were also identified. Discussion: The findings observed in the present study can help to deepen the understanding of the underlying disease mechanisms of AD and enable the development of precision medicine and personalized treatment approaches.
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