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).