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.

Background Hepatocellular carcinoma (HCC) is one of the deadliest cancers and is mainly developed from chronic liver diseases such as hepatitis-B infection-associated liver cirrhosis (LC). The progression from LC to HCC makes the detection of diagnostic biomarkers to be challenging. Hence, there have been constant efforts to improve on identifying the critical and predictive changes accompanying the disease progression. Methods In this study, we looked to using the mass spectrometry mediated spatial metabolomics technique to simultaneous examine hundreds of metabolites in an untargeted fashion. Additionally, metabolic profiles were compared between six subregions within the HCC tissue to collect spatial information. Results Through those metabolites, altered metabolic pathways in LC and HCC were identified. Specifically, the amino acid metabolisms and the glycerophospholipid metabolisms experienced the most changes. Many of the altered metabolites and metabolic pathways were able to be connected through the urea cycle. Conclusions The identification of the key metabolites and pathways can expand our knowledge on HCC metabolic reprogramming and help us exam potential biomarkers for earlier detection of the malignant disease progression.