FIGURE 6 Determination of biomarker combinations for distinguishing primary and secondary tumors. A Receiver operating characteristic (ROC) curves for all biomarker combination models for discriminating BrMs (L.C) from gliomas (IV) based on Monte-Carlo cross-validation (MCCV). Partial least squares discriminant analysis (PLS-DA) was used as the classification method, and PLS-DA built-in was selected as the feature-ranking method with two set latent variables.B Predictive accuracies with different features (top 5, 10, 15, 25, 50, and 100 proteins) based on the ROC curves (A). CPredicted class probabilities (average of the cross-validation) for each sample using a 5-biomarker combination model. Due to balanced subsampling, the classification boundary is at the center (x = 0.5, dotted line). D Protein intensities of five markers (KRT8, KRT19, KRT7, TACSTD2, and CDH1) in both groups. Box plots represent the median and IQR, and the whiskers represent the 1–99 percentile (two-tailed Student’s t-test, * P < 0.05, ** P< 0.01, *** P < 0.001, and **** P< 0.0001).