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Network Biomarkers for Alzheimer’s Disease via a Graph-based Approach

Network-based approaches are an emerging field in the biomedical domain since various complex biological processes and complex human diseases can be interpreted more effectively through biological networks. Identifying groups of interconnected molecular components causing perturbations in the entire network topology for a given under-study disease, these can be considered as potential disease biomarkers, alternatively as «Network Biomarkers». Although there has been remarkable progress with computational systems-based approaches in the road for network biomarkers discovery, this field is still in its infancy, especially in quite complex diseases such as Alzheimer’s Disease (AD). Towards this direction, we proposed a network-based approach for the identification of AD-related network biomarkers. The core of the methodology is a constructed intracellular network integrated with RNA-sequencing expression profiles. Following various functional and topological rules, interconnected co-expressed subnetworks were exported, with differential expression during the transition from a control to a disease state. Isolating the most active subnetworks from the entire network topology, we offer potential network biomarkers that may cause an under-study disease. The methodology was applied using RNA-seq data from a study that examined the behavior of gamma oscillations before the cognitive decline in an AD mouse model. Our results were promising by opening new roads for new frameworks under the perspective of Network Biomarkers discovery.