Biomarker-Driven Analysis Using High-Throughput Approaches in Neuroinflammation and Neurodegenerative Diseases

Abstract

Aging is responsible for homeostatic dysregulation and the primary risk for neurodegenerative diseases. The main signaling pathways may regulate inflammatory-related disorders and neurodegeneration include genomic instability, cell senescence, and mitochondria dysfunction. The use of high-throughput technologies has emerged as a powerful approach to the rapid discovery of many candidate biomarkers for age-related diseases. Various types of molecules, such as nucleic acids, proteins, or metabolites, can serve as soluble factors in clinical practice with deviations in their normal biological levels being an indication of an underlying disease state. The development of multifactorial biomarkers based on models involving molecular alterations in complex disorders may also provide specific challenges for translating biological findings and targeted diagnostic tools. As diseases are often regulated by a multiset of markers that coordinate and interact each other in a complex signaling network to maintain holistic processes within a cell, potent network-based approaches to data-driven biomarker identification are required. System-based biomarker discovery pipelines can offer an extraordinary adjustment opportunity for data heterogeneity and limitation, whereas integrated analysis of distinct networks clusters  can provide important information for the early detection of intracellular pathogenic processes as well as for monitoring the response to treatment.