ArticleFrontiers in aging neuroscience2022
Identification of Alzheimer's Disease Molecular Subtypes Based on Parallel Large-Scale Sequencing.
Article in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Regulatory role of LncRNA FMR1-AS1 in the pathogenesis of alzheimer's disease based on bioinformatics and in vitro experimental validation.Scientific reports · 2025Article
- Single-Nucleus Landscape of Glial Cells and Neurons in Alzheimer's Disease.Molecular neurobiology · 2025Article
- Nutrigenomic influence of a curcumin-supplemented high glycemic diet on hippocampal microvasculature in male C57BL/6J mice.Frontiers in nutrition · 2025Article
- Elucidation of the effects of 2,5-hexandione as a metabolite ofToxicological research · 2024Article
- Identification of altered immune cell types and molecular mechanisms in Alzheimer's disease progression by single-cell RNA sequencing.Frontiers in aging neuroscience · 2024Article
- Exploration of novel biomarkers in Alzheimer's disease based on four diagnostic models.Frontiers in aging neuroscience · 2023Article
- Multi-omic integration via similarity network fusion to detect molecular subtypes of ageing.Brain communications · 2023Article
- REPS1 as a Potential Biomarker in Alzheimer's Disease and Vascular Dementia.Frontiers in aging neuroscience · 2022Article
- Potential biomarkers of Alzheimer's disease and cerebral small vessel disease.Frontiers in molecular neuroscience · 2022Article
- Identification of molecular signatures associated with sleep disorder and Alzheimer's disease.Frontiers in psychiatry · 2022Article
- Exploring Early Physical Examination Diagnostic Biomarkers for Alzheimer's Disease Based on Least Absolute Shrinkage and Selection Operator.Computational and mathematical methods in medicine · 2022Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The incidence of Alzheimer's disease (AD) is constantly increasing as the older population grows, and no effective treatment is currently available. In this study, we focused on the identification of AD molecular subtypes to facilitate the development of effective drugs. AD sequencing data collected from the Gene Expression Omnibus (GEO) database were subjected to cluster sample analysis. Each sample module was then identified as a specific AD molecular subtype, and the biological processes and pathways were verified. The main long non-coding RNAs and transcription factors regulating each "typing pathway" and their potential mechanisms were determined using the RNAInter and TRRUST databases. Based on the marker genes of each "typing module," a classifier was developed for molecular typing of AD. According to the pathways involved, five sample clustering modules were identified (mitogen-activated protein kinase, synaptic, autophagy, forkhead box class O, and cell senescence), which may be regulated through multiple pathways. The classifier showed good classification performance, which may be useful for developing novel AD drugs and predicting their indications.
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