ReviewCancers2025
Detection of Protein and Metabolites in Cancer Analyses by MALDI 2000-2025.
Review in Cancers, 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
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
4 citing papers in PubMed.
- MALDI-MSI Profiling of Effusion Cytology Cell Blocks Distinguishes High-Grade Serous Ovarian Carcinoma from Benign Effusions.Cancers · 2026Article
- Advanced technologies of single-cell metabolomics unveiling cellular metabolic heterogeneity for biological and biomedical research.Journal of food and drug analysis · 2026Review
- The MALDI Method to Analyze the Lipid Profile, Including Cholesterol, Triglycerides and Other Lipids.Current issues in molecular biology · 2026Review
- Natural products as metabolic modulators to enhance cancer immunotherapy: reprogramming the tumor microenvironment.Frontiers in immunology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer metabolomics has become a powerful way of understanding tumor biology, identifying biomarkers and metabolites, and helping precision oncology. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS), among many other analytical platforms, has gained popularity over the past two and a half decades due to its unique ability of directly analyzing metabolites in tissue with spatial resolution. This review will study 2000-2025 MALDI-based strategies for cancer metabolite detection, spanning from early proof-of-concept protein profiling to the development of high-resolution MALDI-MS imaging (MALDI-MSI), which is capable of mapping thousands of metabolites at near single-cell resolution. Its applications include the differentiation of tumor versus normal tissue, discovery of stage and subtype specific biomarkers, mapping of metabolic heterogeneity, and the visualization of drug metabolism in situ. Breakthrough technological milestones, such as the advanced matrices, on-tissue derivatization, MALDI-2 post-ionization, and the integration with Orbitrap or Fourier-transform ion cyclotron resonance (FT-ICR) platforms, have significantly improved the overall sensitivity, metabolite coverage, and spatial fidelity. Clinically, MALDI-MS has shown its purpose in breast, prostate, colorectal, lung, and liver cancers by providing metabolic fingerprints that are linked to tumor microenvironments, hypoxia, and therapeutic response. However, challenges such as the inclusion of matrix interface with low-mass metabolites, limited quantitation, ion suppression, and the lack of standardized procedures do not yet allow for the transition from translation to routine diagnostics. Even with these hurdles, the future of MALDI-MS in oncology remains in a good position with major advancements in multimodal imaging, machine learning-based data integration, portable sampling devices, and clinical validation studies that are pushing the field towards precision treatment.
Indexed as
Identifiers
What Socratic 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 Socratic graph.