MicroRNA expression profiles as biomarkers of minor salivary gland inflammation and dysfunction in Sjögren's syndrome

微小RNA表达谱作为干燥综合征中小唾液腺炎症和功能障碍的生物标志物

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Abstract

OBJECTIVE: MicroRNA reflect physiologic and pathologic processes and may be used as biomarkers of concurrent pathophysiologic events in complex settings such as autoimmune diseases. We generated microRNA microarray profiles from the minor salivary glands of control subjects without Sjögren's syndrome (SS) and patients with SS who had low-grade or high-grade inflammation and impaired or normal saliva production, to identify microRNA patterns specific to salivary gland inflammation or dysfunction. METHODS: MicroRNA expression profiles were generated by Agilent microRNA arrays. We developed a novel method for data normalization by identifying housekeeping microRNA. MicroRNA profiles were compared by unsupervised mathematical methods to test how well they distinguish between control subjects and various subsets of patients with SS. Several bioinformatics methods were used to predict the messenger RNA targets of the differentially expressed microRNA. RESULTS: MicroRNA expression patterns accurately distinguished salivary glands from control subjects and patients with SS who had low-degree or high-degree inflammation. Using real-time quantitative polymerase chain reaction, we validated 2 microRNA as markers of inflammation in an independent cohort. Comparing microRNA from patients with preserved or low salivary flow identified a set of differentially expressed microRNA, most of which were up-regulated in the group with decreased salivary gland function, suggesting that the targets of microRNA may have a protective effect on epithelial cells. The predicted biologic targets of microRNA associated with inflammation or salivary gland dysfunction identified both overlapping and distinct biologic pathways and processes. CONCLUSION: Distinct microRNA expression patterns are associated with salivary gland inflammation and dysfunction in patients with SS, and microRNA represent a novel group of potential biomarkers.

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