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C. Perez-Sánchez et al.
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Figure 3. A circulating miRNA signature in antiphospholipid syndrome (APS) might have potential value as biomarkers of disease. (A) Selected microRNAs (miRNAs or miR) were analyzed in the whole cohort, including 90 APS patients and 42 healthy donors, and reciprocal ratios were performed. Beeswarm plot of each differen- tially expressed miR ratio is shown, along with mean, Standard Deviation, and P-value. For statistical analysis, after normality and equality of variance tests, compar- isons were made by paired Student t-test or a non-parametric test (Mann-Whitney rank sum test). (B) A combination of the 10 miRNA ratios as a panel was carried out by using logistic regression on the data set. ROC curve of miRNA panel and cut off were generated based on the predicted probability (P) for each subject as a single score. The equation used in our model was: “Combined miRNA-ratio panel [Logit(p)] = - 0.64 + 0.034x(miR-19b/miR-34a) + 1.061x(miR-19b/miR-15a) + 0.248x(miR-19b/miR-124) – 1.704x(miR-19b/miR-145) + 2.34x(miR-20a/miR-145) – 0.729x(miR-20a/miR-374a) – 0.624x(miR-20a/miR-210) + 0.088x(miR- 20a/miR-133b) + 0.166x(miR-206/miR-34a) + 0.056x(mir-124/miR-296)”. The area under the curve (AUC), sensitivity and specificity are displayed, and a cut-off value with higher specificity was selected.
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fied as potential mRNA targets of those miRNAs, were quantified in the plasma of APS patients and HDs. As pre- viously reported,20-23 APS patients showed significantly increased plasma levels of TF, PAI-1, MCP-1, VEGF-A and VEGFR-1 (Online Supplementary Figure S1).
Circulating miRNA signature as potential biomarkers of disease in APS
It has been shown that the combination of miRNAs improves their predictive potential to differentiate two pathological conditions.14-19 Thus, to assess the potential of specific circulating miRNAs in APS patients as biomarkers of disease features, reciprocal ratios of the miRNAs ana- lyzed were performed by using statistical tools. By this approach, we identified 10 miRNA ratios, integrated by the 11 selected miRNAs, and differentially expressed in plasma of APS patients in comparison with HDs, includ- ing miR-19b/miR-34a, miR-19b/miR-15a, miR19b/miR- 124, miR-19b/miR-145, miR-20a/miR-145, miR-20a/miR- 374a, miR-20a/miR-210, miR-20a/miR-133b, miR- 206/miR-34a and miR-124/miR-296 (Figure 3A). To fur- ther explore the efficiency of these biomarkers to identify
APS patients, a combination of the 10 miRNA ratios as a panel was carried out by using a logistic regression on the data set, as previously described.24 Thus, all miRNA-ratios were integrated into a single model or equation, which provided a single ‘score’ that allowed us to perform the ROC analysis and establish the cut off for prediction. The ROC curve for the 10 miRNA ratios signature revealed a marked accuracy, evidenced by an AUC of 0.81. At the optimal cut-off value of 0.6, the sensitivity and specificity of the combined miRNA panel for APS identification were 78% and 80%, respectively (Figure 3B).
Stability of miRNA expression profile over time in APS
Plasma from 21 APS patients included in the study was evaluated again three months after the first blood sample collection to analyze the stability of the circulating miRNA profile. Results demonstrated that miRNA expression in the second sample collection did not change in relation to the first analysis (Online Supplementary Figure S2A). Moreover, the levels of miRNA ratios at baseline correlated significantly with the levels of these ratios three months later (Online
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