Zhang Zhihao, Liu Xiangtao, Zhang Suixia, Song Zhixin, Lu Ke, Yang Wenzhong
School of Computer Science and Technology, Xinjiang University, Ürümqi, China.
College of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi, China.
Front Neurosci. 2024 Feb 20;18:1358998. doi: 10.3389/fnins.2024.1358998. eCollection 2024.
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.
阿尔茨海默病（AD）是一种进行性神经退行性疾病，全球有超过5000万老年人受其影响。尽管AD的发病机制尚未完全明确，但基于目前的研究，研究人员能够识别出可能作为对抗AD有效靶点的潜在生物标志物基因和蛋白质。本文旨在全面概述AD生物标志物识别的最新进展，重点介绍各种算法的应用、相关生物学过程的探索以及与共病共享生物标志物的研究。此外，本文还对研究文献中报道的关键基因进行了统计分析，并确定了与来自AlzGen、GeneCard和DisGeNet等数据库的AD相关基因集的交集。对于这些基因集，除了富集分析外，还利用蛋白质-蛋白质相互作用（PPI）网络来识别重叠基因中的核心基因。对多组重叠基因进行了基于GTEx数据库的富集分析、蛋白质相互作用网络分析和组织特异性连通性分析。我们的工作为更好地理解AD的分子机制和更准确地识别关键AD标志物奠定了基础。
