Bioinformatic analysis of proteomic data for iron, inflammation, and hypoxic pathways in restless legs syndrome

Objective/Background We performed bioinformatic analysis of proteomic data to identify the biomarkers of restless legs syndrome (RLS) and provide insights into the putative pathomechanisms, including iron deficiency, inflammation, and hypoxic pathways. Patients/methods Patients with drug-naive idiopathic RLS were recruited at a university hospital from June 2017 to February 2018. Serum samples from patients with RLS (n = 7) and healthy sex- and age-matched controls (n = 6) were evaluated by proteomic analysis. For differentially expressed proteins (DEPs) in patients with RLS, compared to those in controls, the expression profiles and protein–protein interaction (PPI) network were characterized between dysregulated proteins and extracted proteins involved in iron deficiency, hypoxia, and inflammation responses using the String database (http://string-DB.org). The PPI network was visualized by Cytoscape ver. 3. 7. 1. Statistical analyses of the validation Western blot assays were performed using a Student's t-test. Results Interactome network analysis revealed a relationship among the eight proteins, their associated genes, and 150, 47, and 11 proteins related to iron deficiency, inflammation, and hypoxic pathways, respectively. All DEPs were well associated with inflammation, and complement 3, complement C4A, alpha-2 HS glycoprotein, and alpha-2 macroglobulin precursor were found to be in hub positions of networks involved in PPIs including iron deficiency, hypoxia pathway, and inflammation. C3 and C4A were verified using western blotting. Conclusions We identified key molecules that represent the selected cellular pathways as protein biomarkers by PPI network analysis. Changes in inflammation can mediate or affect the pathomechanism of RLS and can thus act as systemic biomarkers.