FIGURE 5 Proteomic differences between primary and metastatic brain tumors.A Comparison of BrMs (n = 14) to gliomas (n = 13) identified 352 DEPs (two-tailed Student’s t -test, P< 0.01 and fold change > 2 or < 0.5).B Principal component analysis (PCA) of BrM (n = 14) and glioma (n = 13) samples based on 352 DEPs. The 95% confidence regions are shown. C GSEA identified downregulated and upregulated proteins in the two groups. The top 20 annotations of GO-BP, Reactome, WikiPathways, and KEGG pathways are shown (Benjamini−Hochberg FDR method, adjusted P < 0.01).
3.5 Diagnosticclassification model for gliomas and BrMs
To illustrate the capacity of proteomic profiling as a powerful prognostic tool for discriminating glioma and BrM tumors, we attempted the diagnostic classification model. To construct a more precise model, malignant brain tumors from primary (WHO grade IV gliomas, glioma (IV), n = 10) and secondary (metastases from L.C, BrM (L.C), n = 10) tumors were adopted for further analysis, which resulted in 265 DEPs (Figure S5A and B). The DEPs in BrM (L.C) versus glioma (IV) and BrM versus glioma showed high overlap (Figure S5C). In line with the above GSEA results, the most notable pathways of the 265 DEPs were enriched in cell movement (Figure S5D). Of these, 37 proteins accounted for 5.23% of the proteins in the Rho GTPase signaling pathway (R-HSA-194315 and R-HSA-9716542), and 32 proteins comprised up to 8.02% of the proteins in the regulation of the actin filament-based process (GO: 0032970). These results were consistent with the migratory and invasive characteristics of malignant cells.
We next used multivariate receiver operating characteristic (ROC) curve analysis based on partial least squares discriminant analysis (PLS-DA). The above 265 differentially expressed variables were analyzed to obtain the optimal and most economical biomarker combination. Five variables (KRT8, KRT19, KRT7, TACSTD2, and CDH1) reached the most economical and optimal area under the curve (AUC) of 0.973 (95% confidence interval [CI] = 0.803–1) (Figure 6A). To evaluate the reliability of the machine-learning strategy, confusion matrices were generated, and the results demonstrated that different samples were correctly classified with 90% accuracy (Figure 6B and C). Notably, these five proteins were significantly upregulated in the BrM (L.C) samples compared to the glioma (IV) samples (Figure 6D).