Trindade Fábio, Nogueira-Ferreira Rita, Bastos Paulo, Amado Francisco, Ferreira Rita, Vitorino Rui
RISE-Health, Department of Surgery and Physiology, Faculty of Medicine, University of Porto, Porto, Portugal.
LAQV-REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro, Portugal.
Methods Mol Biol. 2025;2914:275-302. doi: 10.1007/978-1-0716-4462-1_20.
Cerebrospinal fluid (CSF) is a source of valuable information concerning brain disorders. The technical advances of high throughput omics platforms to analyze body fluids can generate a huge amount of data, whose translation of the biological meaning can be a challenge. Several bioinformatics tools have emerged to help handle this data from a systems biology perspective. Herein, we describe a step-by-step tutorial for CSF proteome data analysis in the set of neurodegenerative diseases using: (i) ShinyGO webtool to perform functional enrichment analysis envisioning the characterization of the biological pathways and processes deregulated in neurodegenerative diseases including Alzheimer's and Parkinson's diseases; (ii) Cytoscape to map disease-specific proteins based on evidence from proteomics; (iii) DisGeNET to identify the proteins more strongly and more specifically associated with neurodegenerative diseases to date; (iv) STRING to identify putative therapeutic targets through a combined protein-protein interaction and network topological analyses. This step-by-step guide might help researchers to better characterize disease pathogenesis and to identify putative disease biomarkers and therapeutic targets.
脑脊液（CSF）是有关脑部疾病的宝贵信息来源。用于分析体液的高通量组学平台的技术进步能够产生海量数据，而解读这些数据的生物学意义可能具有挑战性。已经出现了几种生物信息学工具来从系统生物学角度帮助处理这些数据。在此，我们描述了一套针对神经退行性疾病中脑脊液蛋白质组数据分析的分步教程，使用：（i）ShinyGO网络工具进行功能富集分析，以设想在包括阿尔茨海默病和帕金森病在内的神经退行性疾病中失调的生物途径和过程的特征；（ii）Cytoscape根据蛋白质组学证据绘制疾病特异性蛋白质图谱；（iii）DisGeNET识别迄今为止与神经退行性疾病关联更强且更具特异性的蛋白质；（iv）STRING通过蛋白质 - 蛋白质相互作用和网络拓扑分析的组合来识别潜在的治疗靶点。本分步指南可能有助于研究人员更好地表征疾病发病机制，并识别潜在的疾病生物标志物和治疗靶点。
