Preliminary Study of MALDI-TOF Mass Spectrometry-based Screening of Patients with the NSCLC Serum-Specific Peptides

Abstract

Background and objective The improved survival of patients with lung cancer depends on early diagnosis of lung cancer. However, the traditional diagnostic techniques have several limitations. Mass spectrometry (MS) has been applied as a core technology for cancer diagnosis in preliminary proteomic studies. The aim of this study is to explore the differences in the serum peptide levels of patients with non-small cell lung cancer (NSCLC) and healthy individuals using matrix-assisted laser desorption/ionization (MALDI)-time-of-flight (TOF)-MS. A NSCLC serum classification model was then established. Methods One hundred and thirty three cases of patients with NSCLC serum specimens and 132 cases of healthy human serum specimens were randomly divided into two groups in accordance with the ratio of three to one without age and gender differences. The training group was used to establish the classification model, this group included serum samples from 100 NSCLC cases and 100 healthy individuals. The test group for validating the proposed model was composed of the remaining serum samples from 33 NSCLC cases and 32 healthy individuals. Peptides were extracted from the samples using magnetic beads- immobilized metal affinity capture - copper, and their mass spectra were obtained using an automated MALDI-TOF-MS system. The MS data from the training group was analyzed using the ClinproToolTM software to identify the individual peptide fragments and establish the classification model. The sensitivity and specificity of the model were verified by blind testing with the test group. Results Among the 131 different peptide peaks, ranging from m/z 1,000 Da to 10,000 Da, 14 peaks were significantly different in the NSCLC samples of the training group, as compared with the controls (P<0.000,001; AUC≥0.9); these included 2 higher peaks and 12 lower peaks. The classification model was established, and the test group was verified for only 3 peptide peaks (7,478.59, 2,271.44 and 4,468.38 Da), which were selected by the statistical software. Blind testing revealed that the proposed method had 100% sensitivity, 96.9% specificity and 98.5% accuracy. Conclusion Our results showed that the serum peptide levels were significantly different between NSCLC patients and healthy individuals. A serum peptide-based classification of NSCLC patients was established using an automated MALDI-TOF-MS system. This method demonstrated high sensitivity and specificity in a small-scale test. Future studies should test the proposed model through mass validation. The model could be compared or combined with traditional diagnostic methods to establish novel techniques for the early diagnosis of patients with NSCLC.

DOI: 10.3779/j.issn.1009-3419.2013.05.04