Identification of serum biomarkers for lung cancer using magnetic bead-based SELDI-TOF-MS

Aim: To identify novel serum biomarkers for lung cancer diagnosis using magnetic bead-based surface-enhanced laser desorption/ionization time-of-flight mass spectrum (SELDI-TOF-MS). Methods: The protein fractions of 121 serum specimens from 30 lung cancer patients, 30 pulmonary tuberculosis patients and 33 healthy controls were enriched using WCX magnetic beads and subjected to SELDI-TOF-MS. The spectra were analyzed using Biomarker Wizard version 3.1.0 and Biomarker Patterns Software version 5.0. A diagnostic model was constructed with the marker proteins using a linear discrimination analysis method. The validity of this model was tested in a blind test set consisted of 8 randomly selected lung cancer patients, 10 pulmonary tuberculosis patients and 10 healthy volunteers. Results: Seventeen m/z peaks were identified, which were significantly different between the lung cancer group and the control (tuberculosis and healthy control) groups. Among these peaks, the 6445, 9725, 11705, and 15126 m/z peaks were selected by the Biomarker Pattern Software to construct a diagnostic model for lung cancer. This four-peak model established in the training set could discriminate lung cancer patients from non-cancer patients with a sensitivity of 93.3% (28/30) and a specificity of 90.5% (57/63). The diagnostic model showed a high sensitivity (75.0%) and a high specificity (95%) in the blind test validation. Database searching and literature mining indicated that the featured 4 peaks represented chaperonin (M9725), hemoglobin subunit beta (M15335), serum amyloid A (M11548), and an unknown protein. Conclusion: A lung cancer diagnostic model based on bead-based SELDI-TOF-MS has been established for the early diagnosis or differential diagnosis of lung cancers.