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 Bio-marker 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.