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MALDI-TOF MS as evolving cancer diagnostic tool: a review
Miguel Angel Merlos Rodrigo1, Ondrej Zitka1, Sona Krizkova1
1Department of Chemistry and Biochemistry, Faculty of Agronomy, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic; Central European Institute of Technology, Brno University of Technology, Technicka 3058/10, CZ-616 00 Brno, Czech Republic.
Matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) mass spectrometry aids in cancer diagnostics by discovering and monitoring protein biomarkers. This review covers MALDI-TOF techniques for detecting cancer biomarkers from 2011-2013.
Area of Science:
- Clinical proteomics
- Analytical chemistry
- Biomarker discovery
Background:
- Mass spectrometry advancements are crucial for disease diagnosis.
- Clinical proteomics offers reliable methods for biomarker discovery and monitoring.
- Matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) is a key technology in this field.
Purpose of the Study:
- To review MALDI-TOF profiling techniques for cancer biomarker detection.
- To highlight recent advances in MALDI-TOF applications for cancer diagnostics.
- To explore the potential of MALDI-TOF in revolutionizing cancer diagnosis.
Main Methods:
- Analysis of healthy and cancerous tissues using MALDI-TOF mass spectrometry imaging.
- Identification of cancer-specific proteomic changes.
- Review of MALDI-TOF profiling techniques and recent literature (2011-2013).
Main Results:
- MALDI-TOF enables efficient biomarker discovery and monitoring in clinical proteomics.
- MALDI-TOF imaging can identify cancer-specific proteomic signatures in tissues.
- The technique has potential for revolutionizing cancer diagnostics through biomarker quantification and tissue imaging.
Conclusions:
- MALDI-TOF mass spectrometry is a powerful tool for cancer biomarker discovery and diagnostics.
- MALDI-TOF profiling techniques are valuable for detecting cancer biomarkers across various cancer types.
- Continued advancements in MALDI-TOF hold significant promise for improving clinical cancer diagnosis and monitoring.
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