Serum profiling by mass spectrometry combined with bioinformatics for the biomarkers discovery in diffuse large B-cell lymphoma.

The aim of this study was to identify potential serum biomarkers of diffuse large B-cell lymphoma (DLBCL) and to detect DLBCL therapy response biomarkers. DLBCL serum proteomic analysis was performed using the CM10 ProteinChip mass spectrometry (SELDI-TOF-MS) approach combined with bioinformatics. A total of 178 samples were analyzed in this study from untreated early stage DLBCL patients (38), patients with inflammatory lymphadenopathy (13), healthy donors (35), post-treatment non-relapsed DLBCL patients (53), and relapsed DLBCL patients (39). Model 1 formed by nine protein peaks (m/z: 6443, 5913, 6198, 4098, 7775, 9293, 5946, 5977, and 4628) could be used to distinguish DLBCL patients from healthy individuals with an accuracy of 95.89% (70/73). The diagnostic pattern constructed using the support vector machine including the nine proteins of model 1, showed a maximum Youden's Index. Model 2 formed by three protein peaks (m/z: 3942, 6639, and 4121) could be used to distinguish DLBCL patients from those with inflammatory lymphadenopathy with an accuracy of 94.12% (48/51). Model 3 formed by six protein peaks could distinguish patients with inflammatory lymphadenopathy from healthy individuals with an accuracy of 97.92% (47/48). Model 4 could be used to distinguish non-relapsed DLBCL patients from relapsed DLBCL patients with an accuracy of 84.78% (78/92). The four patterns were validated by leave-one-out cross-validation. These data demonstrate that the CM10 ProteinChip and SELDI-TOF-MS approach combined with bioinformatics can be used effectively to screen for the differential protein expression profiles of DLBCL patients and to predict the response to therapy.