Analysis of the raw serum peptidomic pattern in glioma patients.

BACKGROUND

Glioma is a common and lethal type of brain tumor. Serum peptides reflected the pathological changes of the body. Here we studied the serum peptide profiles to distinguish glioma disease and measure glioma staging.

METHODS

Serum peptides were captured by WCX magnetic beads and were analyzed by MALDI-TOF mass spectrometer. Sera from 53 glioma patients and 69 age-matched healthy controls were analyzed. Clinpro Tools software was used to obtain a common peak m/z list from all measured samples. An optimal subset of peptides was selected to establish a predictive classification model with the newly developed competitive adaptive reweighted sampling (CARS) variable selection method. Serum peptide profiles were classified through a partial least-squares-linear discriminate analysis (PLS-LDA). We also searched for progressively different peptide peaks that correlated with an increasing malignancy of glioma.

RESULTS

The following pattern recognition equation was established with selected peptide signals: Y=-0.1113-0.113X1-0.2916X2+0.1128X3-0.2057X4-0.2047X5-0.3048X6+0.2835X7+0.3121X8-0.1458X9+0.0354X10-0.2022X11. Using this pattern, classification sensitivity and specificity achieved were 0.9057 and 0.9855, respectively. Additionally, we detected 3 peptide signals that correlated with glioma grade. Among these, the intensity of peak 2082.32 Da correlated positively with the glioma progressing, and peaks with sizes of 3316.08 Da and 6631.45 Da show a decreasing intensity with increasing glioma grade.

CONCLUSIONS

11 peptide recognition patterns and specific peak intensities might be useful for the early detection and tumor staging of glioma, but they need to be further validated and evaluated independently in clinical settings.