Decoding Alzheimer's Disease With Depression: Molecular Insights and Therapeutic Target

The purpose of this study was to recognise predictive biomarkers and explore the promising therapeutic targets of AD with depression. We confirmed a positive correlation between AD and depression through MR Analysis. Through WGCNA analysis, we identified 1569 genes containing two modules, which were most related to AD. In addition, 1629 depressive DEGs were also identified. In these genes, 84 genes were shared by both AD and depression, which were screened by the Degree algorithm, MCC algorithm, and four machine learning algorithms. Two genes (ITGB5 and SPCS1) were confirmed as predictive biomarkers with AUC > 0.7. Furthermore, the nomogram indicated that ITGB5 and SPCS1 are good biomarkers in diagnosing AD with depression. Four drugs targeted at ITGB5 were determined by the DGIdb website. In conclusion, we identified two predictive biomarkers for AD with depression, thus providing promising therapeutic targets for AD with depression.

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Publication Details

Journal
Journal of Cellular and Molecular Medicine
Published
2025-03-01
DOI
https://doi.org/10.1111/jcmm.70454
Citations
2
Primary Topic
Alzheimer's disease research and treatments
Type
article
Field-Weighted Citation Impact
2.33

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article

Decoding Alzheimer's Disease With Depression: Molecular Insights and Therapeutic Target

Yihao Ge, Feng Zhang, Fang Dong, Zekun Li et al.
2 citations
Journal of Cellular and Molecular Medicine
Alzheimer's disease research and treatments
2.33
article

Decoding Alzheimer's Disease With Depression: Molecular Insights and Therapeutic Target

Yihao Ge, Feng Zhang, Fang Dong, Zekun Li, Xiaohan Li, Hongmin Guo
article en
2 citations

Abstract

The purpose of this study was to recognise predictive biomarkers and explore the promising therapeutic targets of AD with depression. We confirmed a positive correlation between AD and depression through MR Analysis. Through WGCNA analysis, we identified 1569 genes containing two modules, which were most related to AD. In addition, 1629 depressive DEGs were also identified. In these genes, 84 genes were shared by both AD and depression, which were screened by the Degree algorithm, MCC algorithm, and four machine learning algorithms. Two genes (ITGB5 and SPCS1) were confirmed as predictive biomarkers with AUC > 0.7. Furthermore, the nomogram indicated that ITGB5 and SPCS1 are good biomarkers in diagnosing AD with depression. Four drugs targeted at ITGB5 were determined by the DGIdb website. In conclusion, we identified two predictive biomarkers for AD with depression, thus providing promising therapeutic targets for AD with depression.

Journal of Cellular and Molecular MedicineVol. 29(5)
Hebei Medical University (CN), Third Hospital of Hebei Medical University (CN)
National Natural Science Foundation of China
Good health and well-being
Openalex Percentile: Top 14%
Alzheimer's disease research and treatments
2.33
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