阿尔茨海默病：网络医学视角的洞察。

Alzheimer's disease: insights from a network medicine perspective.

机构信息

Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy.

Department of Computer, Control and Management Engineering, Sapienza University of Rome, Rome, Italy.

出版信息

Sci Rep. 2022 Oct 7;12(1):16846. doi: 10.1038/s41598-022-20404-3.

DOI:10.1038/s41598-022-20404-3

PMID:36207441

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9546925/

Abstract

Alzheimer's disease (AD) is the most common neurodegenerative disease that currently lacks available effective therapy. Thus, identifying novel molecular biomarkers for diagnosis and treatment of AD is urgently demanded. In this study, we exploited tools and concepts of the emerging research area of Network Medicine to unveil a novel putative disease gene signature associated with AD. We proposed a new pipeline, which combines the strengths of two consolidated algorithms of the Network Medicine: DIseAse MOdule Detection (DIAMOnD), designed to predict new disease-associated genes within the human interactome network; and SWItch Miner (SWIM), designed to predict important (switch) genes within the co-expression network. Our integrated computational analysis allowed us to enlarge the set of the known disease genes associated to AD with additional 14 genes that may be proposed as new potential diagnostic biomarkers and therapeutic targets for AD phenotype.

摘要

阿尔茨海默病（AD）是目前尚无有效治疗方法的最常见的神经退行性疾病。因此，迫切需要确定用于 AD 诊断和治疗的新型分子生物标志物。在这项研究中，我们利用新兴的网络医学研究领域的工具和概念，揭示了与 AD 相关的新的潜在疾病基因特征。我们提出了一种新的管道，它结合了网络医学中两种已确立算法的优势：DIseAse MOdule Detection（DIAMOnD），用于预测人类相互作用网络中与疾病相关的新基因；以及 SWItch Miner（SWIM），用于预测共表达网络中重要的（切换）基因。我们的综合计算分析使我们能够将与 AD 相关的已知疾病基因集扩大到另外 14 个基因，这些基因可能被提议作为 AD 表型的新的潜在诊断生物标志物和治疗靶标。