Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Corfu, Greece.
Adv Exp Med Biol. 2023;1423:207-214. doi: 10.1007/978-3-031-31978-5_19.
System-level network-based approaches are an emerging field in the biomedical domain since biological networks can be used to analyze complicated biological processes and complex human disorders more efficiently. Network biomarkers are groups of interconnected molecular components causing perturbations in the entire network topology that can be used as indicators of pathogenic biological processes when studying a given disease. Although in the last years computational systems-based approaches have gained ground on the path to discovering new network biomarkers, in complex diseases like Alzheimer's disease (AD), this approach has still much to offer. Especially the adoption of single-cell RNA sequencing (scRNA-seq) has now become the dominant technology for the study of stochastic gene expression. Toward this orientation, we propose an R workflow that extracts disease-perturbed subpathways within a pathway network. We construct a gene-gene interaction network integrated with scRNA-seq expression profiles, and after network processing and pruning, the most active subnetworks are isolated from the entire network topology. The proposed methodology was applied on a real AD-based scRNA-seq data, providing already existing and new potential AD biomarkers in gene network context.
基于系统水平的网络方法是生物医学领域的一个新兴领域，因为生物网络可用于更有效地分析复杂的生物过程和复杂的人类疾病。网络生物标志物是一组相互关联的分子成分，它们会导致整个网络拓扑结构发生扰动，当研究给定疾病时，这些标志物可作为致病生物过程的指标。尽管在过去几年中，基于计算的系统方法在发现新的网络生物标志物方面取得了进展，但在阿尔茨海默病（AD）等复杂疾病中，这种方法仍然有很多优势。特别是单细胞 RNA 测序（scRNA-seq）的采用，现在已成为研究随机基因表达的主导技术。为此，我们提出了一种 R 工作流程，用于从途径网络中提取受疾病干扰的亚途径。我们构建了一个整合了 scRNA-seq 表达谱的基因-基因相互作用网络，并且在进行网络处理和修剪后，从整个网络拓扑结构中分离出最活跃的子网。所提出的方法应用于基于真实 AD 的 scRNA-seq 数据，在基因网络背景下提供了已有的和新的潜在 AD 生物标志物。
