Identification of novel risk genes for Alzheimer’s disease by integrating genetics from hippocampus

Alzheimer's disease (AD) stands as the most prevalent neurodegenerative ailment, presently lacking a definitive cure. Given that primary medications for AD patients in the early or middle stages demonstrate optimal efficacy, it becomes crucial to delve into the identification of risk genes associated with early onset. In our study, we compiled and integrated three transcriptomics datasets (GSE48350, GSE36980, GSE5281) originating from the hippocampus of 37 AD patients and 66 healthy controls (CTR) for comprehensive bioinformatics analysis. Comparative analysis with CTR revealed 25 up-regulated genes and 291 down-regulated genes in AD. Those down-regulated genes were notably enriched in processes related to the transmission and transport of synaptic signals. Intriguingly, 27 differentially expressed genes implicated in AD were also correlated with the Braak stage, establishing a connection with various immune cell types that exhibit differences in AD, including cytotoxic T cells, neutrophils, CD4 T cells, Th1, Th2, and Tfh. Significantly, a Cox model, constructed using nine feature genes, effectively stratified AD samples (HR = 2.72, 95% CI 1.94 ~ 3.81, P = 3.6e-10), highlighting their promising potential for risk assessment. In conclusion, our investigation sheds light on novel genes intricately linked to the onset and progression of AD, offering potential biomarkers for the early detection of this debilitating condition. This study contributes valuable insights toward enhancing the strategies for preventing and treating AD.

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Publication Details

Journal
Scientific Reports
Published
2024-11-11
DOI
https://doi.org/10.1038/s41598-024-78181-0
Citations
4
Primary Topic
Alzheimer's disease research and treatments
Type
article
Field-Weighted Citation Impact
1.31

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article

Identification of novel risk genes for Alzheimer’s disease by integrating genetics from hippocampus

Jie Li, Kun‐Ho Song, Shenghui Hu, Lingfang Li et al.
4 citations
Scientific Reports
Alzheimer's disease research and treatments
1.31
article

Identification of novel risk genes for Alzheimer’s disease by integrating genetics from hippocampus

Jie Li, Kun‐Ho Song, Shenghui Hu, Lingfang Li, Shanshan Cai
article en
4 citations

Abstract

Alzheimer's disease (AD) stands as the most prevalent neurodegenerative ailment, presently lacking a definitive cure. Given that primary medications for AD patients in the early or middle stages demonstrate optimal efficacy, it becomes crucial to delve into the identification of risk genes associated with early onset. In our study, we compiled and integrated three transcriptomics datasets (GSE48350, GSE36980, GSE5281) originating from the hippocampus of 37 AD patients and 66 healthy controls (CTR) for comprehensive bioinformatics analysis. Comparative analysis with CTR revealed 25 up-regulated genes and 291 down-regulated genes in AD. Those down-regulated genes were notably enriched in processes related to the transmission and transport of synaptic signals. Intriguingly, 27 differentially expressed genes implicated in AD were also correlated with the Braak stage, establishing a connection with various immune cell types that exhibit differences in AD, including cytotoxic T cells, neutrophils, CD4 T cells, Th1, Th2, and Tfh. Significantly, a Cox model, constructed using nine feature genes, effectively stratified AD samples (HR = 2.72, 95% CI 1.94 ~ 3.81, P = 3.6e-10), highlighting their promising potential for risk assessment. In conclusion, our investigation sheds light on novel genes intricately linked to the onset and progression of AD, offering potential biomarkers for the early detection of this debilitating condition. This study contributes valuable insights toward enhancing the strategies for preventing and treating AD.

Scientific ReportsVol. 14(1)
Central South University (CN), Hunan University (CN), Xiangya Hospital Central South University (CN)
Hunan University
Openalex Percentile: Top 18%
Alzheimer's disease research and treatments
1.31
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