Longitudinal profiling of serum ADAM17 across clinical stages in multiple myeloma: a dynamic biomarker and its association with T cell alterations

Background Multiple myeloma (MM) is the second most common hematologic malignancy in adults. Owing to marked heterogeneity in pathogenesis, clinical presentation and prognosis, the 5-year survival rate remains approximately 50%. Robust and clinically actionable biomarkers are therefore urgently needed to refine risk stratification, guide therapeutic decisions and improve prognostic accuracy. ADAM17 (A Disintegrin and Metalloproteinase-17) plays a pivotal role in inflammation, tissue homeostasis and tumorigenesis. Although the research in MM was limited, the existing evidence suggests that ADAM17 may be involved in the pathological process of the disease. Methods In this study, the serum ADAM17 concentrations of 26 MM patients at three different clinical stages (newly diagnosed, remission and progression) were detected by ELISA, and the correlation between ADAM17 concentrations and clinical parameters was analyzed. The expression levels of CD62L on bone marrow T cells of MM patients and healthy donors were compared by flow cytometry. In addition, the above findings were verified in the expanded cohort using the GEO public data set. Results Serum ADAM17 concentrations increased progressively from remission to progression and at diagnosis. Levels aligned with DS, ISS and R-ISS staging systems and were strongly associated with renal function. Compared with healthy controls, T cells from MM patients displayed significantly reduced CD62L expression across CD3 + , CD4 + and CD8 + subsets, with the most pronounced loss on CD8 + T cells. CIBERSORT analysis revealed significantly higher bone-marrow infiltration of CD8 + T cells in patients with low versus high ADAM17 expression. Conclusion Our data identify ADAM17 as an easily quantifiable, longitudinal biomarker that concurrently reflects tumor development stage and renal function damage in MM patients. Incorporation of ADAM17 into existing risk algorithms may enhance prognostic precision and enable earlier, patient-tailored intervention.