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.
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 cancerpatients, 30 pulmonary tuberculosispatients 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 cancerpatients, 10 pulmonary tuberculosispatients 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 cancerpatients from non-cancerpatients 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.
Authors: Sven Baumann; Uta Ceglarek; Georg Martin Fiedler; Jan Lembcke; Alexander Leichtle; Joachim Thiery Journal: Clin Chem Date: 2005-04-21 Impact factor: 8.327
Authors: John B Welsh; Lisa M Sapinoso; Suzanne G Kern; David A Brown; Tao Liu; Asne R Bauskin; Robyn L Ward; Nicholas J Hawkins; David I Quinn; Pamela J Russell; Robert L Sutherland; Samuel N Breit; Christopher A Moskaluk; Henry F Frierson; Garret M Hampton Journal: Proc Natl Acad Sci U S A Date: 2003-03-06 Impact factor: 11.205
Authors: Emanuel F Petricoin; Ali M Ardekani; Ben A Hitt; Peter J Levine; Vincent A Fusaro; Seth M Steinberg; Gordon B Mills; Charles Simone; David A Fishman; Elise C Kohn; Lance A Liotta Journal: Lancet Date: 2002-02-16 Impact factor: 79.321
Authors: Andrea Ardizzoni; Mara A Cafferata; Marcello Tiseo; Rosangela Filiberti; Paola Marroni; Francesco Grossi; Michela Paganuzzi Journal: Cancer Date: 2006-12-15 Impact factor: 6.860
Authors: Zhen Zhang; Robert C Bast; Yinhua Yu; Jinong Li; Lori J Sokoll; Alex J Rai; Jason M Rosenzweig; Bonnie Cameron; Young Y Wang; Xiao-Ying Meng; Andrew Berchuck; Carolien Van Haaften-Day; Neville F Hacker; Henk W A de Bruijn; Ate G J van der Zee; Ian J Jacobs; Eric T Fung; Daniel W Chan Journal: Cancer Res Date: 2004-08-15 Impact factor: 12.701
Authors: Mary A De Groote; Payam Nahid; Leah Jarlsberg; John L Johnson; Marc Weiner; Grace Muzanyi; Nebojsa Janjic; David G Sterling; Urs A Ochsner Journal: PLoS One Date: 2013-04-18 Impact factor: 3.240