Razavi Newman Integrative Genomics and Bioinformatics Core, Salk Institute for Biological Studies, La Jolla, CA, USA.
Independent Researcher, Tucson, AZ, USA.
Ageing Res Rev. 2022 Nov;81:101721. doi: 10.1016/j.arr.2022.101721. Epub 2022 Aug 25.
Alzheimer's disease (AD) is an incredibly complex and presently incurable age-related brain disorder. To better understand this debilitating disease, we collated and performed a meta-analysis on publicly available RNA-Seq, microarray, proteomics, and microRNA samples derived from AD patients and non-AD controls. 4089 samples originating from brain tissues and blood remained after applying quality filters. Since disease progression in AD correlates with age, we stratified this large dataset into three different age groups: < 75 years, 75-84 years, and ≥ 85 years. The RNA-Seq, microarray, and proteomics datasets were then combined into different integrated datasets. Ensemble machine learning was employed to identify genes and proteins that can accurately classify samples as either AD or control. These predictive inputs were then subjected to network-based enrichment analyses. The ability of genes/proteins associated with different pathways in the Molecular Signatures Database to diagnose AD was also tested. We separately identified microRNAs that can be used to make an AD diagnosis and subjected the predicted gene targets of the most predictive microRNAs to an enrichment analysis. The following key themes emerged from our machine learning and bioinformatics analyses: cell death, cellular senescence, energy metabolism, genomic integrity, glia, immune system, metal ion homeostasis, oxidative stress, proteostasis, and synaptic function. Many of the results demonstrated unique age-specificity. For example, terms highlighting cellular senescence only emerged in the earliest and intermediate age ranges while the majority of results relevant to cell death appeared in the youngest patients. Existing literature corroborates the importance of these hallmarks in AD.
阿尔茨海默病（AD）是一种极其复杂且目前无法治愈的与年龄相关的脑部疾病。为了更好地理解这种使人衰弱的疾病，我们整理并对来自 AD 患者和非 AD 对照的公开可用的 RNA-Seq、微阵列、蛋白质组学和 microRNA 样本进行了荟萃分析。应用质量过滤器后，仍有 4089 个源自脑组织和血液的样本保留下来。由于 AD 的疾病进展与年龄相关，我们将这个大型数据集分为三个不同的年龄组：<75 岁、75-84 岁和≥85 岁。然后将 RNA-Seq、微阵列和蛋白质组学数据集合并到不同的综合数据集中。采用集成机器学习来识别能够准确将样本分类为 AD 或对照的基因和蛋白质。这些预测输入随后进行基于网络的富集分析。还测试了与分子特征数据库中不同途径相关的基因/蛋白质诊断 AD 的能力。我们分别确定了可用于 AD 诊断的 microRNAs，并对最具预测性的 microRNAs 的预测基因靶标进行了富集分析。我们的机器学习和生物信息学分析得出了以下关键主题：细胞死亡、细胞衰老、能量代谢、基因组完整性、神经胶质、免疫系统、金属离子稳态、氧化应激、蛋白质稳态和突触功能。许多结果表现出独特的年龄特异性。例如，突出细胞衰老的术语仅出现在最早和中间年龄范围内，而与细胞死亡相关的大多数结果出现在最年轻的患者中。现有文献证实了这些特征在 AD 中的重要性。
