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Volume 21, Issue 7
  • ISSN: 1567-2050
  • E-ISSN: 1875-5828

Abstract

Introduction

Alzheimer's Disease (AD) is the most common neurodegenerative disease, and timely and effective diagnosis is essential for the prevention and treatment of AD. Peripheral blood is readily available, inexpensive, and non-invasive, making it an ideal substrate for screening diagnostic biomarkers.

Methods

The Notch signaling pathway is closely related to AD, so genes related to the Notch signaling pathway may be candidate diagnostic biomarkers for AD. Here, we have performed an integrated analysis of peripheral blood cells transcriptomics from two AD cohorts (GSE63060: Ctrl = 104, MCI = 80, AD = 145; GSE63061: Ctrl = 134, MCI = 109, AD = 139) to reveal the expression levels of 16 Notch signals involving 100 genes.

Results

The results have shown the changes in Notch signaling-related genes to be highly consistent in both AD cohorts. Bioinformatics analysis has found Differentially Expressed Genes (DEGs) related to Notch signaling to mainly play important roles in Alzheimer's disease, the Notch signaling pathway, and the C-type lectin receptor signaling pathway. Multiple machine learning analyses have revealed IKBKB, HDAC2, and PIK3R1 to exhibit good diagnostic value in both AD cohorts and that they may be ideal biomarkers for early diagnosis of AD.

Conclusion

This study has provided a comprehensive description of the molecular signatures of the Notch signaling pathway in AD peripheral blood and a potential diagnostic model for AD clinical screening.

© 2024 Bentham Science Publishers
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2024-11-29
2025-01-18

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/content/journals/car/10.2174/0115672050339307241108101528
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Molecular Signatures and Clinical Significance of Notch Signaling Pathway in Peripheral Blood of Patients with Alzheimer's Disease
Curr Alzheimer Res 21, 479 (2024); https://doi.org/10.2174/0115672050339307241108101528
/content/journals/car/10.2174/0115672050339307241108101528
/content/journals/car/10.2174/0115672050339307241108101528
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  • Article Type:     Research Article
Keyword(s): Alzheimer's disease; biomarkers; machine learning; Notch signaling; peripheral blood; transcriptomics

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