ArticleJournal of Alzheimer's disease : JAD2024
Identification of the Shared Gene Signatures Between Alzheimer's Disease and Diabetes-Associated Cognitive Dysfunction by Bioinformatics Analysis Combined with Biological Experiment.
Article in Journal of Alzheimer's disease : JAD, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- SLC38A9 Regulation Affects Hippocampal Neuronal Autophagy: A Potential Alzheimer's Therapeutic Approach by Suppressing Alzheimer's Disease-Related Protein Deposition.CNS neuroscience & therapeutics · 2026Article
- Identification of Key Genes in Esketamine's Therapeutic Effects on Perioperative Neurocognitive Disorders via Transcriptome Sequencing.Drug design, development and therapy · 2025Article
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3 authors.
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Abstract
Background: The connection between diabetes-associated cognitive dysfunction (DACD) and Alzheimer's disease (AD) has been shown in several observational studies. However, it remains controversial as to how the two related. Objective: To explore shared genes and pathways between DACD and AD using bioinformatics analysis combined with biological experiment. Methods: We analyzed GEO microarray data to identify DEGs in AD and type 2 diabetes mellitus (T2DM) induced-DACD datasets. Weighted gene co-expression network analysis was used to find modules, while R packages identified overlapping genes. A robust protein-protein interaction network was constructed, and hub genes were identified with Gene ontology enrichment and Kyoto Encyclopedia of Genome and Genome pathway analyses. HT22 cells were cultured under high glucose and amyloid-β 25-35 (Aβ25-35) conditions to establish DACD and AD models. Quantitative polymerase chain reaction with reverse transcription verification analysis was then performed on intersection genes. Results: Three modules each in AD and T2DM induced-DACD were identified as the most relevant and 10 hub genes were screened, with analysis revealing enrichment in pathways such as synaptic vesicle cycle and GABAergic synapse. Through biological experimentation verification, 6 key genes were identified. Conclusions: This study is the first to use bioinformatics tools to uncover the genetic link between AD and DACD. GAD1, UCHL1, GAP43, CARNS1, TAGLN3, and SH3GL2 were identified as key genes connecting AD and DACD. These findings offer new insights into the diseases' pathogenesis and potential diagnostic and therapeutic targets.
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