Fig. 4

Construction of a prediction model using plasma sEVs derived miRNAs and CA-125 in the discovery set A: Pearson correlation analysis of 15 selected miRNAs levels detected by Taqman qRT-PCR in the discovery set (R0, n = 15; non-R0, n = 15). Pearson correlation coefficient and P value were displayed in the bottom-left and the upper-right, respectively. B: The log(λ) was plotted versus AUC. Numbers along the upper x-axis indicated the number of predicted factors. The black vertical lines defined the optimal values of λ (λ = 0.07), where the model provided the best fitting to the data; C: The LASSO coefficient profile plot of the selected 4 texture features (miR-320a-3p, miR-378a-3p, miR-1307-3p, let-7d-3p). D: The LASSO coefficient values of 4 miRNAs. E–F: The ROC curves of (E) 4-miRNA panel, CA-125, CA-153, CA-199, and F 4-miRNA combined with CA-125 for detecting residual disease in the discovery set. Maximum classification accuracy was labeled by the red circle. miRNA microRNA, sEVs small extracellular vesicles, R0 advanced ovarian cancer with no residual disease, non-R0 advanced ovarian cancer with any residual disease, LASSO least absolute shrinkage and selection operator, AUC area under the receiver operating characteristic curve, ROC receiver operating characteristic curve; *P < 0.05; ** < 0.01; *** < 0.001; **** < 0.0001