ArticlePloS one2018
MALDI-TOF analysis of blood serum proteome can predict the presence of monoclonal gammopathy of undetermined significance.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- AI-augmented MALDI-TOF MS screening reveals a high burden of undiagnosed monoclonal gammopathy in adult patients.iScience · 2026Article
- Dysregulated immune proteins in plasma in the UK Biobank predict multiple myeloma 12 years before clinical diagnosis.Blood advances · 2025Article
- Liquid biopsy of peripheral blood using mass spectrometry detects primary extramedullary disease in multiple myeloma patients.Scientific reports · 2024Article
- Improved Screening of Monoclonal Gammopathy Patients by MALDI-TOF Mass Spectrometry.Journal of the American Society for Mass Spectrometry · 2023Article
- Liquid biopsy: an evolving paradigm for the biological characterisation of plasma cell disorders.Leukemia · 2021Review
- Use of Matrix-Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry Analysis of Serum Peptidome to Classify and Predict Coronavirus Disease 2019 Severity.Open forum infectious diseases · 2021Article
- Challenges and Opportunities in Clinical Applications of Blood-Based Proteomics in Cancer.Cancers · 2020Review
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Authors and funding
9 authors.
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Abstract
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.
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