Feasibility of using probabilistic methods to analyse microRNA quantitative data in forensically relevant body fluids: a proof-of-principle study.

Several studies have confirmed that microRNAs (miRNAs) are promising markers for body fluid identification since they were introduced to this field. However, there is no consensus on the choice of reference genes and identification strategies. In this study, 13 potential candidate miRNAs were screened from three forensically relevant body fluid datasets, and the expression of 12 markers in five body fluids was determined using a real-time quantitative method. Two probabilistic approaches, Naive Bayes (NB) and partial least squares discriminant analysis (PLS-DA), were then applied to predict the origin of the samples to determine whether probabilistic methods are helpful in body fluid identification using miRNA quantitative data. Furthermore, 14 reference combinations were used to validate the influence of different reference choices on the predicted results simultaneously. Our results showed that in the NB model, leave-one-out cross-validation (LOOCV) achieved 100% accuracy and the prediction accuracy of the test set was 100% in most reference combinations. In the PLS-DA model, the first two components could interpret about 80% expression variance and LOOCV achieved 100% accuracy when miR-92a-3p was used as the reference. This study preliminarily proved that probabilistic approaches hold huge potential in miRNA-based body fluid identification, and the choice of references influences the prediction results to a certain extent.