Abstract
Multiple myeloma (MM) is an incurable plasma cell malignancy accounting for approximately 10% of hematological malignancies. Identification of reliable biomarkers for better diagnosis and prognosis remains a major challenge. This study aimed to identify potential serum prognostic biomarkers corresponding to MM disease activity and evaluate their impact on patient outcomes. Serum proteomic profiles of patients with MM and age-matched controls were performed using LC-MS/MS. In the verification and validation phases, the concentration of the candidate biomarkers was measured using an ELISA technique. In addition, the association of the proposed biomarkers with clinical outcomes was assessed. We identified 23 upregulated and 15 downregulated proteins differentially expressed in newly diagnosed and relapsed/refractory MM patients compared with MM patients who achieved at least a very good partial response to treatment (≥VGPR). The top two candidate proteins, metastasis-associated protein-2 (MTA2) and argonaute-2 (AGO2), were selected for further verification and validation studies. Both MTA2 and AGO2 showed significantly higher levels in the disease-active states than in the remission states (p < 0.001). Regardless of the patient treatment profile, high MTA2 levels were associated with shorter progression-free survival (p = 0.044; HR = 2.48; 95% CI, 1.02 to 6.02). Conversely, high AGO2 levels were associated with IgG and kappa light-chains isotypes and an occurrence of bone involvement features (p < 0.05) and were associated with prolonged time to response (p = 0.045; HR = 3.00; 95% CI, 1.03 to 8.76). Moreover, the analytic results using a publicly available NCBI GEO dataset revealed that AGO2 overexpression was associated with shorter overall survival among patients with MM (p = 0.032, HR = 1.60, 95% CI, 1.04 to 2.46). In conclusion, MTA2 and AGO2 proteins were first identified as potential biomarkers that reflect disease activity, provide prognostic values and could serve as non-invasive indicators for disease monitoring and outcome predicting among patients with MM.
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