Data for: Simultaneous subset tracing and miRNA profiling of tumor-derived exosomes via dual-surface-protein orthogonal barcoding

The clinical potential of miRNA-based liquid biopsy has been largely limited by the heterogeneous sources in plasma and tedious assay processes. Here we develop a precise and robust one-pot assay called dual-surface-protein-guided orthogonal recognition of tumor-derived exosomes and in-situ profiling of microRNAs (SORTER) to detect tumor-derived exosomal miRNAs and enhance the diagnostic accuracy of prostate cancer (PCa). The SORTER utilizes two allosteric aptamers against exosomal marker CD63 and tumor marker EpCAM to create an orthogonal labeling barcode and achieve selective sorting of tumor-specific exosome subtypes. Furthermore, the labeled barcode on tumor-derived exosomes initiated targeted membrane fusion with liposome probes to import miRNA detection reagents, enabling in-situ sensitive profiling of tumor-derived exosomal miRNAs. With a signature of six miRNAs, SORTER differentiated PCa and benign prostatic hyperplasia with an accuracy of 100%. Notably, the diagnostic accuracy reached 90.6% in the classification of metastatic and non-metastatic PCa. We envision that the SORTER will promote the clinical adaptability of miRNA-based liquid biopsy.

Methods
Mean, SD, and LOD were calculated with standard formulas. Significance tests were obtained via a two-tailed Student’s t-test. The intensities of individual miRNA markers detected by the SORTER approach used min-max normalization. The PCa signature was calculated as the weighted sum of the normalized intensities of six miRNA markers by LDA, respectively. For binary classification, P values for pairwise comparisons were performed using a nonparametric, two-tailed Mann-Whitney U test. For ternary classification, the overall and group pair P values were determined using Kruskal-Wallis one-way ANOVA with post hoc Dunn’s test for pairwise multiple comparisons. Hierarchical clustering was performed for the analysis markers using the “pheatmap” package in the R language. ROC analyses were constructed for individual markers or marker combinations to evaluate the AUC, sensitivity and specificity, and accuracy of cancer diagnosis. The training cohort (n = 42) was first analyzed to generate the discriminant function model, which was used to classify the patients in the validation cohort (n = 32). The optimal cutoff points were selected using Youden’s index based on the training cohort, which was applied to evaluate the sensitivity, specificity, and accuracy of the validation cohort. The t-distributed stochastic neighbor embedding (t-SNE) was performed using six markers as the input for binary classification (PCa and BPH).