Journal
FRONTIERS IN ONCOLOGY
Volume 12, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fonc.2022.891018
Keywords
spatial metabolomics; cancer heterogeneity; breast cancer; esophageal cancer; glioblastoma; MALDI-MSI; DESI-MSI; lung cancer
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Funding
- Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties [SZGSP001]
- Guangxi Key Laboratory ofMetabolic Diseases Research [20-065-76]
- Shenzhen Key Medical Discipline Construction Fund [SZXK059]
- science andtechnology plan of Shenzhen [JCYJ20180306140810282]
- National Natural Science Foundation of China [82003172]
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Metabolic heterogeneity of cancer has significant impact on its treatment outcomes and prognosis. Spatial metabolomics using mass spectrometry imaging technology has shown promise in studying altered metabolites in cancer patients, allowing for successful classification of cancer tissue subtypes and identification of distribution trends of key metabolites.
Metabolic heterogeneity of cancer contributes significantly to its poor treatment outcomes and prognosis. As a result, studies continue to focus on identifying new biomarkers and metabolic vulnerabilities, both of which depend on the understanding of altered metabolism in cancer. In the recent decades, the rise of mass spectrometry imaging (MSI) enables the in situ detection of large numbers of small molecules in tissues. Therefore, researchers look to using MSI-mediated spatial metabolomics to further study the altered metabolites in cancer patients. In this review, we examined the two most commonly used spatial metabolomics techniques, MALDI-MSI and DESI-MSI, and some recent highlights of their applications in cancer studies. We also described AFADESI-MSI as a recent variation from the DESI-MSI and compare it with the two major techniques. Specifically, we discussed spatial metabolomics results in four types of heterogeneous malignancies, including breast cancer, esophageal cancer, glioblastoma and lung cancer. Multiple studies have effectively classified cancer tissue subtypes using altered metabolites information. In addition, distribution trends of key metabolites such as fatty acids, high-energy phosphate compounds, and antioxidants were identified. Therefore, while the visualization of finer distribution details requires further improvement of MSI techniques, past studies have suggested spatial metabolomics to be a promising direction to study the complexity of cancer pathophysiology.
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