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oai:repisalud.isciii.es:20.500.12105/226052024-11-29T01:21:03Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16967
Repisalud author Barcelo, Francisca author Gomila, Rosa author de Paul, Ivan author Gili, Xavier author Segura, Jaume author Pérez-Montaña, Albert author Jimenez-Marco, Teresa author Sampol Mayol, Antonia author Portugal, Jose 2024-09-06T09:56:40Z 2024-09-06T09:56:40Z 2018-08-02 Barcelo F, Gomila R, De Paúl Bernal I, Gili X, Segura Fuster J, Perez Montaña A, et al. MALDI-TOF analysis of blood serum proteome can predict the presence of monoclonal gammopathy of undetermined significance. PLoS One. 2018 Aug 02;13(8):e0201793. 10.1371/journal.pone.0201793 1932-6203 PloS One http://hdl.handle.net/20.500.13003/9174 30071092 L623301107 2-s2.0-85050907577 https://hdl.handle.net/20.500.12105/22605 440778600101 Monoclonal gammopathy of undetermined significance (MGUS) is a plasma cell dyscrasia that can progress to malignant multiple myeloma (MM). Specific molecular biomarkers to classify the MGUS status and discriminate the initial asymptomatic phase of MM have not been identified. We examined the serum peptidome profile of MGUS patients and healthy volunteers using MALDI-TOF mass spectrometry and developed a predictive model for classifying serum samples. The predictive model was built using a support vector machine (SVM) supervised learning method tuned by applying a 20-fold cross-validation scheme. Predicting class labels in a blinded test set containing randomly selected MGUS and healthy control serum samples validated the model. The generalization performance of the predictive model was evaluated by a double cross-validation method that showed 88% average model accuracy, 89% average sensitivity and 86% average specificity. Our model, which classifies unknown serum samples as belonging to either MGUS patients or healthy individuals, can be applied to clinical diagnosis. eng MALDI-TOF analysis of blood serum proteome can predict the presence of monoclonal gammopathy of undetermined significance research article