Characterization of the plasma proteome of multiple myeloma and its precursor conditions and identification of a prognostic high-risk signature of progression
Analysis reveals significant protein markers in plasma, suggesting machine learning may enhance prognostic classification in multiple myeloma patients.
Key Points
High levels of plasma proteins indicate potential markers for disease progression and risk in multiple myeloma.
A machine learning classifier correctly identified 97% of SMM/MM samples from healthy samples, aiding in diagnosis.
Comprehensive proteomic profiling was executed on 462 plasma samples, leveraging Olink® technology for detailed analysis.
Identifying a five-protein signature linked to disease progression may enhance patient monitoring with non-invasive methods.