Background & objective: The progress in proteomics provides a novel platform for early diagnosis of cancer, and screening for new tumor biomarkers. This study was designed to develop and evaluate a diagnostic model of breast cancer with surface enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS) ProteinChip array technology and artificial neural network software.
Methods: SELDI-TOF-MS ProteinChip was used to detect serum protein patterns of 49 patients with breast cancer, and 33 healthy women. Diagnostic model was developed, and validated using artificial neural network software.
Results: An intact diagnostic model from all 253 discrepant protein peaks, and a terse model from the top-scored 4 peaks were built. The diagnostic sensitivity, and specificity of the intact model were 83.33% (15/18), and 88.89% (8/9)u the detection rates of breast cancer of stage I, and stage II-IV using the intact model were 90.00% (9/10), and 75.00% (6/8). The diagnostic sensitivity, and specificity of the terse model were 76.47% (13/17), and 90.00% (9/10)u the detection rates of breast cancer of stage I, and stage II-IV using the terse model were 100.00% (3/3), and 71.43% (10/14). The diagnostic values of these 2 models were similar (P>0.05). Their diagnostic abilities to breast cancer of stage I were not worse than those to breast cancer of stage II-IV (P>0.05).
Conclusion: High sensitivity and specificity achieved by this method show great potential for early diagnosis of breast cancer, and screening for new tumor biomarkers.