Qing-Tao Wang and Yong-Zhe Li contributed equally to this work.
Construction of A Multiple Myeloma Diagnostic Model by Magnetic Bead-Based MALDI-TOF Mass Spectrometry of Serum and Pattern Recognition Software
Article first published online: 19 MAR 2009
Copyright © 2009 Wiley-Liss, Inc.
The Anatomical Record
Volume 292, Issue 4, pages 604–610, April 2009
How to Cite
Wang, Q.-T., Li, Y.-Z., Liang, Y.-F., Hu, C.-J., Zhai, Y.-H., Zhao, G.-F., Zhang, J., Li, N., Ni, A.-P., Chen, W.-M. and Xu, Y. (2009), Construction of A Multiple Myeloma Diagnostic Model by Magnetic Bead-Based MALDI-TOF Mass Spectrometry of Serum and Pattern Recognition Software. Anat Rec, 292: 604–610. doi: 10.1002/ar.20871
- Issue published online: 19 MAR 2009
- Article first published online: 19 MAR 2009
- Manuscript Accepted: 5 DEC 2008
- Manuscript Received: 14 APR 2008
- multiple myeloma;
- diagnostic model;
- magnetic beads
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. Anat Rec, 292:604–610, 2009. © 2009 Wiley-Liss, Inc.