ArticleCell division2025
Detection of early relapse in multiple myeloma patients.
Article in Cell division, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Sex-specific dysregulation of exosomal non-coding RNAs drives multiple myeloma progression.Blood cancer journal · 2025Article
- A rare dermatological manifestation of follicular spicules in a patient with multiple myeloma and end-stage renal disease on hemodialysis: A case report.Experimental and therapeutic medicine · 2025Article
- Navigating the Landscape of Exosomal microRNAs: Charting Their Pivotal Role as Biomarkers in Hematological Malignancies.Non-coding RNA · 2025Review
- Innovations in MALDI-TOF Mass Spectrometry: Bridging modern diagnostics and historical insights.Open life sciences · 2025Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
backgroundMultiple 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.
resultsUsing 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.
conclusionMass spectrometry coupled with machine learning shows potential as a reliable, rapid, and cost-effective preliminary screening technique to supplement current diagnostics.
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Registered trials
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