Identification of TAC1 Associated with Alzheimer's Disease Using a Robust Rank Aggregation Approach

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.

A review and analysis of key biomarkers in Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects over 50 million elderly individuals worldwide. Although the pathogenesis of AD is not fully understood, based on current research, researchers are able to identify potential biomarker genes and proteins that may serve as effective targets against AD. This article aims to present a comprehensive overview of recent advances in AD biomarker identification, with highlights on the use of various algorithms, the exploration of relevant biological processes, and the investigation of shared biomarkers with co-occurring diseases. Additionally, this article includes a statistical analysis of key genes reported in the research literature, and identifies the intersection with AD-related gene sets from databases such as AlzGen, GeneCard, and DisGeNet. For these gene sets, besides enrichment analysis, protein-protein interaction (PPI) networks utilized to identify central genes among the overlapping genes. Enrichment analysis, protein interaction network analysis, and tissue-specific connectedness analysis based on GTEx database performed on multiple groups of overlapping genes. Our work has laid the foundation for a better understanding of the molecular mechanisms of AD and more accurate identification of key AD markers.