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MALDI-TOF Mass Spectrometry

MALDI-TOF Mass Spectrometry

Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.Matrix-assisted laser desorption ionization (MALDI) is a commonly...

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Updated: May 14, 2026

Utilizing 18F-FDG PET/CT Imaging and Quantitative Histology to Measure Dynamic Changes in the Glucose Metabolism in Mouse Models of Lung Cancer

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Deciphering tumor metabolites: emerging technologies shaping clinical implications.

Xiao-Hui Zhu¹, Liang Huang¹, Pu Tian¹

¹Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, PR China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, PR China.

Trends in Cancer

|May 12, 2026

Summary

Metabolite detection technologies are advancing, revealing new roles in tumor progression and offering potential for cancer diagnosis and treatment. Overcoming current limitations requires next-generation tools and deep learning for better clinical application.

Keywords:

cancer clinical translation metabolites metabolomics

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Area of Science:

Oncology
Metabolomics
Biochemistry

Background:

Metabolites significantly influence tumor progression through diverse biological effects.
Understanding metabolite functions is crucial for advancing cancer research and treatment.

Purpose of the Study:

To review current and emerging technologies for metabolite detection and functional analysis in cancer.
To explore the clinical applications of metabolites in oncology.
To identify limitations and future directions in the field.

Main Methods:

Comprehensive literature review of metabolite detection platforms and functional research strategies.
Analysis of emerging technologies and advanced algorithms for metabolite analysis.
Evaluation of clinical applications in diagnosis, patient stratification, and therapeutic target discovery.

Main Results:

Innovations have identified novel metabolites and their previously unrecognized functions in tumor biology.
Advanced algorithms facilitate clinical applications of metabolites.
Methodological constraints and tumor metabolic heterogeneity present significant challenges.

Conclusions:

Next-generation technologies are needed to overcome current limitations in metabolite detection and functional analysis.
Deep learning frameworks are essential for enhancing functional insights and clinical translation of metabolomics in oncology.
Further research is required to fully harness the potential of metabolites in cancer care.