Identification of Bone Marrow and Peripheral Blood Plasma Extracellular Vesicle Protein Biomarker Signatures for...

Multiple myeloma (MM) is a hematological malignancy characterized by the clonal proliferation of abnormal plasma cells within the bone marrow (BM). Despite advances in treatment that have improved survival, the disease remains incurable. MM diagnosis requires invasive bone marrow biopsy to quantify the percentage of malignant plasma cells. In this study, the potential of extracellular vesicles (EVs) as a non-invasive liquid biopsy for MM diagnosis and staging was investigated, highlighting the diagnostic value of their proteomic biomarker cargo.

Plasma-derived EVs from peripheral blood and bone marrow of 33 MM patients and 12 healthy donors were isolated, and their proteomic content was profiled via mass spectrometry. Biomarker signatures were identified using supervised machine learning to predict monoclonal gammopathy of undetermined significance (MGUS), progression to symptomatic MM, and relapse. Their discriminatory power was further evaluated through receiver operating characteristic curve analysis, and complementary performance metrics, including accuracy, sensitivity, specificity, predictive values, and F1 score. Significantly altered proteins were additionally assessed for functional enrichment in relevant biological pathways.

The analysis identified a six-protein biomarker signature, forming four optimal logistic regression diagnostic MM peripheral blood models with predictive accuracies of >85% and areas under the curve of >0.91. The signature was characterized by increased abundance of APOC1 and LGALS1 and decreased abundance of S100A7, CD226, ALAD, and KRT78, reflecting immune modulation, impaired immune surveillance, and disrupted proteostatic pathways.

The performance of the identified proteins supports their potential as a minimally invasive EV-based liquid biopsy in MM diagnosis and monitoring, warranting future validation.