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Detection of early relapse in multiple myeloma patients

T. Růžičková, M. Vlachová, L. Pečinka, M. Brychtová, M. Večeřa, L. Radová, S. Ševčíková, M. Jarošová, J. Havel, L. Pour, S. Ševčíková

. 2025 ; 20 (1) : 4. [pub] 20250129

Status neindexováno Jazyk angličtina Země Anglie, Velká Británie

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/bmc25008542

Grantová podpora
MUNI/A/1587/2023 Masarykova Univerzita
FNBr, 65269705 University Hospital Brno
AZV NU21-03-00076 Ministerstvo Zdravotnictví Ceské Republiky
Programme EXCELES, ID Project No. LX22NPO5102 European Union

BACKGROUND: Multiple myeloma (MM) represents the second most common hematological malignancy characterized by the infiltration of the bone marrow by plasma cells that produce monoclonal immunoglobulin. While the quality and length of life of MM patients have significantly increased, MM remains a hard-to-treat disease; almost all patients relapse. As MM is highly heterogenous, patients relapse at different times. It is currently not possible to predict when relapse will occur; numerous studies investigating the dysregulation of non-coding RNA molecules in cancer suggest that microRNAs could be good markers of relapse. RESULTS: Using small RNA sequencing, we profiled microRNA expression in peripheral blood in three groups of MM patients who relapsed at different intervals. In total, 24 microRNAs were significantly dysregulated among analyzed subgroups. Independent validation by RT-qPCR confirmed changed levels of miR-598-3p in MM patients with different times to relapse. At the same time, differences in the mass spectra between groups were identified using matrix-assisted laser desorption/ionization time of flight mass spectrometry. All results were analyzed by machine learning. CONCLUSION: Mass spectrometry coupled with machine learning shows potential as a reliable, rapid, and cost-effective preliminary screening technique to supplement current diagnostics.

Citace poskytuje Crossref.org

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