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).