Combining micro-RNA and protein sequencing to detect robust biomarkers for Graves' disease and orbitopathy

Graves' Disease (GD) is an autoimmune condition in which thyroid-stimulating antibodies (TRAB) mimic thyroid-stimulating hormone function causing hyperthyroidism. 5% of GD patients develop inflammatory Graves' orbitopathy (GO) characterized by proptosis and attendant sight problems. A major challenge is to identify which GD patients are most likely to develop GO and has relied on TRAB measurement. We screened sera/plasma from 14 GD, 19 GO and 13 healthy controls using high-throughput proteomics and miRNA sequencing (Illumina's HiSeq2000 and Agilent-6550 Funnel quadrupole-time-of-flight mass spectrometry) to identify potential biomarkers for diagnosis or prognosis evaluation. Euclidean distances and differential expression (DE) based on miRNA and protein quantification were analysed by multidimensional scaling (MDS) and multinomial regression respectively. We detected 3025 miRNAs and 1886 proteins and MDS revealed good separation of the 3 groups. Biomarkers were identified by combined DE and Lasso-penalized predictive models; accuracy of predictions was 0.86 (±0:18), and 5 miRNA and 20 proteins were found including Zonulin, Alpha-2 macroglobulin, Beta-2 glycoprotein 1 and Fibronectin. Functional analysis identified relevant metabolic pathways, including hippo signaling, bacterial invasion of epithelial cells and mRNA surveillance. Proteomic and miRNA analyses, combined with robust bioinformatics, identified circulating biomarkers applicable to diagnose GD, predict GO disease status and optimize patient management.