title: Construction of a multiple myeloma diagnostic model by magnetic bead-based MALDI-TOF mass spectrometry of serum and pattern recognition software.
abstract: 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.
authors.affiliation: Department of Clinical Laboratory, Chao Yang Hospital, Capital Medical University, Beijing, China.
authors.forename: Qing-Tao
authors.initials: QT
authors.lastname: Wang
authors.forename: Yong-Zhe
authors.initials: YZ
authors.lastname: Li
authors.forename: Yu-Fang
authors.initials: YF
authors.lastname: Liang
authors.forename: Chao-Jun
authors.initials: CJ
authors.lastname: Hu
authors.forename: Yu-Hua
authors.initials: YH
authors.lastname: Zhai
authors.forename: Guan-Fei
authors.initials: GF
authors.lastname: Zhao
authors.forename: Jian
authors.initials: J
authors.lastname: Zhang
authors.forename: Ning
authors.initials: N
authors.lastname: Li
authors.forename: An-Ping
authors.initials: AP
authors.lastname: Ni
authors.forename: Wen-Ming
authors.initials: WM
authors.lastname: Chen
authors.forename: Yang
authors.initials: Y
authors.lastname: Xu
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D014408:Biomarkers, Tumor / Q000097:blood; D001798:Blood Proteins / Q000032:analysis; D019295:Computational Biology; D005260:Female; D006801:Humans; D008280:Magnetics / Q000379:methods*; D008297:Male; D008875:Middle Aged; D025202:Molecular Diagnostic Techniques / Q000379:methods*; D009101:Multiple Myeloma / Q000097:blood* / Q000175:diagnosis*; D009363:Neoplasm Proteins / Q000032:analysis / Q000097:blood; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D011379:Prognosis; D020543:Proteome / Q000032:analysis; D040901:Proteomics / Q000379:methods; D012680:Sensitivity and Specificity; D012984:Software / Q000639:trends; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*
chemical_list: D014408:Biomarkers, Tumor; D001798:Blood Proteins; D009363:Neoplasm Proteins; D020543:Proteome