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 （Ml1548）, 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.