Identification of lung cancer patients by serum protein profiling using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry.

Objective: Diagnosis of lung cancer at an early disease stage is important for successful treatment and improving the outcome of patients. To improve its prognosis, we attempted to explore novel tools for screening serum biomarkers to distinguish lung cancer from healthy individuals by serum protein profiles and a classification tree algorithm.

Methods: Serum samples were applied to metal affinity protein chips to generate mass spectra by surface-enhanced laser desorption/ionization (SELDI) time-of-flight mass spectrometry. Protein peak identification and clustering were performed using the Biomarker Wizard software. Proteomic spectra of serum samples from 89 lung cancer patients and age- and sex-matched 68 healthy individuals were used as a training set and a classification tree with 3 distinct protein masses was generated by using Biomarker Pattern software. The validity of the classification tree was then challenged with a blind test set including another 62 lung cancer patients and 34 healthy individuals. We additionally determined Cyfra21-1 and carcinoembryonic antigen in all the serum samples included in this study using an electrochemiluminescent immunoassay.

Results: The software identified an average of 48 mass peaks/spectrum and 3 of the identified peaks at 5808, 5971, and 7779 d were used to construct the classification tree. The classification tree separated effectively lung cancer from healthy individuals, achieving a sensitivity of 91% (81 of 89) and a specificity of 97% (66 of 68). The blind test challenged the model with a sensitivity of 89% (55 of 62) and a specificity of 91% (31 of 34), and a positive predictive value of 90% (86 of 96), respectively. The specificity of Cyfra21-1 and the sensitivity provided by Cyfra21-1 and carcinoembryonic antigen used individually or in combination were significantly lower than that of the SELDI marker pattern (P < 0.05 or P < 0.005, respectively).

Conclusion: The results suggest that SELDI time-of-flight mass spectrometry technique can correctly distinguish lung cancer patients from healthy individuals and shows great potential for the development of a screening test for the detection of lung cancer.