ArticleJournal of neurology2020
Cerebrospinal fluid-based metabolomics to characterize different types of brain tumors.
Article in Journal of neurology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Cognitive manifestations and brain integrity in hereditary transthyretin amyloidosis: a systematic review.Journal of neurology · 2025Pooled it
- Integrated Proteomic and Metabolomic Analyses of Cerebrospinal Fluid from Pediatric Patients with Diffuse Intrinsic Pontine Glioma.International journal of molecular sciences · 2026Article
- Liquid Biopsy in Primary CNS Tumors: Bridging Biology, Technology, and Clinical Care.Molecular diagnosis & therapy · 2026Review
- Untargeted and Targeted Cerebrospinal Fluid Neurometabolomics via Chromatography-Mass Spectrometry-Based Methods.Molecules (Basel, Switzerland) · 2026Review
- Decoding Brain Metastasis With Liquid Biopsy: Biomarkers and Technologies in EGFR-Mutant NSCLC.Cancer medicine · 2026Review
- The evolving landscape of brain metastasis: volume II.Trends in cancer · 2026Review
- A scalable and reliable deep learning framework for enhanced brain tumor detection and diagnosis using AI-based medical imaging.Frontiers in medicine · 2026Article
- Exploring the role of 338 cerebrospinal fluid metabolites in primary brain malignancies risk: a Mendelian randomization study.Discover oncology · 2025Article
- Metabolic adaptations of brain metastasis.Nature reviews. Cancer · 2025Review
- Evaluating liquid biopsy biomarkers for early detection of brain metastasis: A systematic review.Neuro-oncology practice · 2025Article
- Brain Tumor Detection and Prediction in MRI Images Utilizing a Fine-Tuned Transfer Learning Model Integrated Within Deep Learning Frameworks.Life (Basel, Switzerland) · 2025Article
- Metabolomics in cancer detection: A review of techniques, biomarkers, and clinical utility.BioMedicine · 2025Review
- Metabolomics of cerebrospinal fluid reveals candidate diagnostic biomarkers to distinguish between spinal muscular atrophy type II and type III.CNS neuroscience & therapeutics · 2024Article
- Employing deep learning and transfer learning for accurate brain tumor detection.Scientific reports · 2024Article
- Monitoring central nervous system tumour metabolism using cerebrospinal fluid.Frontiers in oncology · 2024Review
- Metabolomic biomarkers in liquid biopsy: accurate cancer diagnosis and prognosis monitoring.Frontiers in oncology · 2024Review
- To metabolomics and beyond: a technological portfolio to investigate cancer metabolism.Signal transduction and targeted therapy · 2023Review
- High-throughput quantitation of amino acids and acylcarnitine in cerebrospinal fluid: identification of PCNSL biomarkers and potential metabolic messengers.Frontiers in molecular biosciences · 2023Article
- Liquid biopsy: early and accurate diagnosis of brain tumor.Journal of cancer research and clinical oncology · 2022Review
- Liquid biopsies to occult brain metastasis.Molecular cancer · 2022Review
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBrain tumors cause significant morbidity and mortality due to rapid progression and high recurrence risks. Reliable biomarkers to improve diagnosis thereof are desirable.
objectiveThis work aimed to identify panels of biomarkers for diagnostic purposes using cerebrospinal fluid (CSF)-based metabolomics.
methodsA cohort of 163 histologically-proven patients with brain disorders was involved. Comprehensive CSF-based metabolomics was achieved by liquid chromatography-quadrupole time-of-flight spectrometric (LC-Q/TOF-MS) and multivariate statistical analyses. The diagnostic performance of the metabolic markers was evaluated using receiver operating characteristic curves.
resultsA total of 508 ion features were detected by the LC-Q/TOF-MS analysis, of which 27 metabolites were selected as diagnostic markers to discriminate different brain tumor types. The area under the curve (AUC) was 0.91 for lung adenocarcinoma patients with brain metastases (MBT) vs. lung adenocarcinoma patients without brain metastases (NMBT), 0.83 for primary central nervous system lymphoma (PCNSL) vs. secondary central nervous system involvement of systemic lymphoma (SCNSL), 0.77 for PCNSL vs. MBT, 0.87 for SCNSL vs. MBT, 0.86 for MBT vs. nontumorous brain diseases (NT), and 0.80 for PCNSL vs. NT. Perturbed metabolic pathways between the comparisons related mainly to amino acids and citrate metabolism.
conclusionsCSF-based metabolomics to a large extent reliably identifies significant metabolic differences between different brain tumors and shows great potential for diagnosis of brain tumors.
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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.