Construction of a multiple myeloma diagnostic model by magnetic bead-based MALDI-TOF mass spectrometry of serum and pattern recognition software.

A diagnosis of multiple myeloma (MM) is difficult to make on the basis of any single laboratory test result. Accurate diagnosis of MM generally results from a number of costly and invasive laboratory tests and medical procedures. The aim of this work is to find a new, highly specific and sensitive method for MM diagnosis. Serum samples were tested in groups representing MM (n = 54) and non-MM (n = 108). These included a subgroup of 17 plasma cell dyscrasias, a subgroup of 17 reactive plasmacytosis, 5 B cell lymphomas, and 7 other tumors with osseus metastasis, as well as 62 healthy donors as controls. Bioinformatic calculations associated with MM were performed. The decision algorithm, with a panel of three biomarkers, correctly identified 24 of 24 (100%) MM samples and 46 of 49 (93.88%) non-MM samples in the training set. During the masked test for the discriminatory model, 26 of 30 MM patients (sensitivity, 86.67%) were precisely recognized, and all 34 normal donors were successfully classified; patients with reactive plasmacytosis were also correctly classified into the non-MM group, and 11 of the other patients were incorrectly classified as MM. The results suggested that proteomic fingerprint technology combining magnetic beads with MALDI-TOF-MS has the potential for identifying individuals with MM. The biomarker classification model was suitable for preliminary assessment of MM and could potentially serve as a useful tool for MM diagnosis and differentiation diagnosis.

Promising diagnostic biomarkers for primary biliary cirrhosis identified with magnetic beads and MALDI-TOF-MS.

(PBC) is not a rare disease worldwide. Most patients are diagnosed at the advanced stage, primarily because there are not yet any valid biomarkers available for early diagnosis. Useful biomarkers are absolutely necessary for early detection of PBC. Fortunately, the use of MALDI-TOF-MS and pattern recognition software has been successful in finding specific markers for the early detection of the disease. To screen for potential protein biomarkers in the serum for diagnosing PBC, MALDI-TOF-MS combined with magnetic beads and pattern recognition software was used to investigate 119 serum samples from 44 patients with PBC, 32 controls with other hepatic disease, and 43 healthy controls. A total of 69 discriminant m/z peaks were identified as being associated with PBC. Of them, the m/z peaks at 3445, 4260, 8133, and 16,290 were used to construct a model for the diagnosis of PBC. This diagnostic model can distinguish PBC from non-PBC controls with a sensitivity of 93.3% and a specificity of 95.1%. In our blind test, it demonstrated good sensitivity and specificity: 92.9% and 82.4%, respectively. These results indicate that useful serum biomarkers for PBC can be discovered by MALDI-TOF-MS combined with the use of magnetic beads and pattern recognition software. The pattern of multiple markers provides a powerful and reliable diagnostic method for PBC with high sensitivity and specificity.