BACKGROUND: There are currently no reliable biomarkers to predict relapse in Graves' disease (GD). In the present study, we investigated novel diagnostic biomarkers to predict the long-term remission of or relapse in GD. METHODS: A DNA microarray analysis was performed to examine gene expression in the peripheral leukocytes of a frequently relapsing patient with GD and a patient in long-term remission after the discontinuation of antithyroid drugs (ATDs). Based on the DNA microarray analysis, we focused on Sialic acid-binding immunoglobulin-like lectin1 (SIGLEC1) as a candidate novel biomarker to predict GD relapse. Three hundred and fifty-eight patients with GD in the thyroid clinics of four different hospitals in Japan were included in a cross-sectional study to establish whether SIGLEC1 mRNA levels distinguish GD relapse experience from long-term remission. An additional 55 patients with GD were enrolled in a prospective study to clarify whether SIGLEC1 mRNA levels at ATD discontinuation predict GD relapse. RESULTS: SIGLEC1 mRNA levels were significantly higher in patients with GD relapse experience than in those in long-term remission. Based on the receiver operating characteristic analysis, we found that high SIGLEC1 mRNA levels (≥258.9 copies) significantly distinguished GD relapse experience from long-term remission (p < 0.0001; sensitivity 66.7%, specificity 70.1%). In the prospective study, when the optimal cutoff value from the receiver operating characteristic curve analysis was applied to SIGLEC1 mRNA positivity at ATD discontinuation, SIGLEC1-positive patients (≥258.9 copies) showed a significantly higher cumulative risk of relapse than SIGLEC1-negative patients (<258.9 copies) (p = 0.022, the log-rank test). CONCLUSIONS: SIGLEC1 mRNA levels have potential as a novel predictive biomarker for GD relapse.
|Number of pages||10|
|Journal||Thyroid : official journal of the American Thyroid Association|
|Publication status||Published - Jan 1 2018|
All Science Journal Classification (ASJC) codes
- Endocrinology, Diabetes and Metabolism