ArticlePloS one2020
Concurrent lipidomics and proteomics on malignant plasma cells from multiple myeloma patients: Probing the lipid metabolome.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Imetelstat-mediated alterations in fatty acid metabolism to induce ferroptosis as a therapeutic strategy for acute myeloid leukemia.Nature cancer · 2024Trial
- Trial
- Novel proteomic characterization of multiple myeloma bone marrow interstitial fluid links prognosis to coagulation pathways.Clinical proteomics · 2025Article
- Acid ceramidase controls proteasome inhibitor resistance and is a novel therapeutic target for the treatment of relapsed/refractory multiple myeloma.Haematologica · 2025Article
- Exploring the role of mitochondrial metabolism and immune infiltration in myocardial infarction: novel insights from bioinformatics and experimental validation.Frontiers in immunology · 2025Article
- Lipid metabolism in multiple myeloma: pathogenesis, therapeutic opportunities, and future directions.Frontiers in oncology · 2025Review
- Review
- Navigating the clinical landscape: Update on the diagnostic and prognostic biomarkers in multiple myeloma.Molecular biology reports · 2024Review
- Development of a Global Metabo-Lipid-Prote-omics Workflow to Compare Healthy Proximal and Distal Colonic Epithelium in Mice.Journal of proteome research · 2024Article
- The proteogenomic landscape of multiple myeloma reveals insights into disease biology and therapeutic opportunities.Nature cancer · 2024Article
- Multi-omics investigation of the resistance mechanisms of pomalidomide in multiple myeloma.Frontiers in oncology · 2023Article
- Intron retention-induced neoantigen load correlates with unfavorable prognosis in multiple myeloma.Oncogene · 2021Article
- The fatty acid elongase ELOVL6 regulates bortezomib resistance in multiple myeloma.Blood advances · 2021Article
- Multi-omics tumor profiling technologies to develop precision medicine in multiple myeloma.Exploration of targeted anti-tumor therapy · 2021Review
- Applications of Lipidomics in Tumor Diagnosis and Therapy.Advances in experimental medicine and biology · 2021Article
- Lipidomic-Based Advances in Diagnosis and Modulation of Immune Response to Cancer.Metabolites · 2020Review
- Choline PET/CT in Multiple Myeloma.Cancers · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
backgroundMultiple myeloma (MM) is a hematological malignancy characterized by the clonal expansion of malignant plasma cells. Though durable remissions are possible, MM is considered incurable, with relapse occurring in almost all patients. There has been limited data reported on the lipid metabolism changes in plasma cells during MM progression. Here, we evaluated the feasibility of concurrent lipidomics and proteomics analyses from patient plasma cells, and report these data on a limited number of patient samples, demonstrating the feasibility of the method, and establishing hypotheses to be evaluated in the future.
methodsPlasma cells were purified from fresh bone marrow aspirates using CD138 microbeads. Proteins and lipids were extracted using a bi-phasic solvent system with methanol, methyl tert-butyl ether, and water. Untargeted proteomics, untargeted and targeted lipidomics were performed on 7 patient samples using liquid chromatography-mass spectrometry. Two comparisons were conducted: high versus low risk; relapse versus newly diagnosed. Proteins and pathways enriched in the relapsed group was compared to a public transcriptomic dataset from Multiple Myeloma Research Consortium reference collection (n = 222) at gene and pathways level.
resultsFrom one million purified plasma cells, we were able to extract material and complete untargeted (~6000 and ~3600 features in positive and negative mode respectively) and targeted lipidomics (313 lipids), as well as untargeted proteomics analysis (~4100 reviewed proteins). Comparative analyses revealed limited differences between high and low risk groups (according to the standard clinical criteria), hence we focused on drawing comparisons between the relapsed and newly diagnosed patients. Untargeted and targeted lipidomics indicated significant down-regulation of phosphatidylcholines (PCs) in relapsed MM. Although there was limited overlap of the differential proteins/transcripts, 76 significantly enriched pathways in relapsed MM were common between proteomics and transcriptomics data. Further evaluation of transcriptomics data for lipid metabolism network revealed enriched correlation of PC, ceramide, cardiolipin, arachidonic acid and cholesterol metabolism pathways to be exclusively correlated among relapsed but not in newly-diagnosed patients.
conclusionsThis study establishes the feasibility and workflow to conduct integrated lipidomics and proteomics analyses on patient-derived plasma cells. Potential lipid metabolism changes associated with MM relapse warrant further investigation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.