Circulating Salivary miRNA hsa-miR-221 as Clinically Validated Diagnostic Marker for Hand, Foot, and Mouth Disease in Pediatric Patients

Figures

• 

  Fig. 1. Overall study design and patient cohorts involved in model development and validations. Screening of potential miRNA biomarkers for HFMD infection was performed on 3 EV71, 3 CA6 and 3 healthy pooled saliva samples. The 8 putative miRNA predictors from primary screen was subsequently used to form diagnostic models using the training set which includes 75% of the “Singapore Cohort” with support vector machines. Cross validation was carried out using k-fold validation method for 10 folds and respective performances of the two models were determined. Blinded validation of the two models was carried out using the test set which includes 25% of the “Singapore Cohort’ and the whole “Taiwan Cohort”.

• 

  Fig. 2. Salivary miRNA expression of HFMD in the salivary miRnome screen. (A) PCA plot based on miRNA expression values across pools (n = 3 each). (B) Heat map showing differentially expressed miRNA gene expression pattern across different samples (n = 3). Volcano plot was used to illustrate distribution of significant and non-significant miRNA in Singapore HFMD Cohort 1 screen in C. Healthy Vs EV71 (n = 6) and D. Healthy Vs CA6 (n = 6). Red colour represent population with absolute 4-fold change while significant miRNAs were represented in yellow or green while red is non-significant. Student t-test (non-parametric) was performed and HITs were determined using absolute fold change of >4-fold and p-value < 0.05.

• 

  Fig. 3. miRNA selection and performance tuning. A. Logistic regression of hit miRNAs. ROC curve was used to display sensitivity and specificity of individual miRNA in HFMD diagnosis with the entire “Singapore Cohort”. B. Overall accuracy of the diagnosis model resolved with increasing number of miRNA classifiers using support vector machine model in the training set of the “Singapore Cohort”. C. Importance of individual miRNA in the 6-miRNA model. Accuracy for each model was calculated using “caret” package in R software. “ggplot2” library was used for illustration using R software.

• 

  Fig. 4. Risk score of healthy and HFMD patients in blinded validation. Risk index of HFMD was obtained for (A) the 6-miRNA model and (B) the 4-miRNA model in testing set of “Singapore Cohort”. The two models were also validated in the “Taiwan Cohort” using (C) the 6-miRNA model and (D) the 4-miRNA model. Circles denote data points with triplicate readings. Box plot was constructed using ggplot2 library in R software.

• 

  Fig. 5. Differential expression of hit miRNAs in the Singapore and Taiwan cohorts. Boxplots of miRNA expressions in healthy (orange), “Singapore HFMD” (green) and “Taiwan HFMD” (blue) cohorts. Stars indicate significance between HFMD and healthy. (***P < 10−3, **P < 10−2, **P < 0.05, FDR-adjusted non-parametric ANOVA with Dunnett's multiple comparison test using 95% CI).