Emerging metabolomic tools to study cancer metastasis

Melo, Luiza Martins Nascentes; Lesner, Nicholas P.; Sabatier, Marie; Ubellacker, Jessalyn M.; Tasdogan, Alpaslan

doi:10.1016/j.trecan.2022.07.003

Cited by 29 publications

(9 citation statements)

References 126 publications

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“…Majority of them was higher in triple negative breast cancer patients compared to estrogen receptor positive breast cancer patients 27 . Enhanced metabolite excretion can serve as potential biomarkers for identifying highly metastatic cancers 28 .…”

Section: Discussionmentioning

confidence: 99%

1H-NMR metabolomics analysis identifies hypoxanthine as a novel metastasis-associated metabolite in breast cancer

Shakartalla,

Ashmawy,

Semreen

et al. 2024

Sci Rep

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Breast cancer is one of the leading causes of death in females, mainly because of metastasis. Oncometabolites, produced via metabolic reprogramming, can influence metastatic signaling cascades. Accordingly, and based on our previous results, we propose that metabolites from highly metastatic breast cancer cells behave differently from less-metastatic cells and may play a significant role in metastasis. For instance, we aim to identify these metabolites and their role in breast cancer metastasis. Less metastatic cells (MCF-7) were treated with metabolites secreted from highly metastatic cells (MDA-MB-231) and the gene expression of three epithelial-to-mesenchymal transition (EMT) markers including E-cadherin, N-cadherin and vimentin were examined. Some metabolites secreted from MDA-MB-231 cells significantly induced EMT activity. Specifically, hypoxanthine demonstrated a significant EMT effect and increased the migration and invasion effects of MCF-7 cells through a hypoxia-associated mechanism. Hypoxanthine exhibited pro-angiogenic effects via increasing the VEGF and PDGF gene expression and affected lipid metabolism by increasing the gene expression of PCSK-9. Notably, knockdown of purine nucleoside phosphorylase, a gene encoding for an important enzyme in the biosynthesis of hypoxanthine, and inhibition of hypoxanthine uptake caused a significant decrease in hypoxanthine-associated EMT effects. Collectively for the first time, hypoxanthine was identified as a novel metastasis-associated metabolite in breast cancer cells and represents a promising target for diagnosis and therapy.

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Section: Discussionmentioning

confidence: 99%

1H-NMR metabolomics analysis identifies hypoxanthine as a novel metastasis-associated metabolite in breast cancer

Shakartalla,

Ashmawy,

Semreen

et al. 2024

Sci Rep

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“… 237 Moreover, SCM could be employed for profiling rare or Circulating tumour cells (CTCs) 238 – 240 as well as for cancer subtypes discrimination and new therapies development. 225 , 241 – 244 …”

Section: Single-cell Metabolic Analysismentioning

confidence: 99%

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

Danzi

Pacchiana

Mafficini

et al. 2023

Sig Transduct Target Ther

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Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.

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“…In particular, multiclass metabolomics has become a hotspot, attracting much attention to the study of the trends of metabolic markers among multiple sample groups . It can directly reveal different process stages of complex diseases, different types of drug action effectiveness, , and differences among similar diseases in the clinic . Moreover, the construction of classification models is an integral part of classifying different samples into multiple classes in multiclass metabolomics .…”

Section: Introductionmentioning

confidence: 99%

MultiClassMetabo: A Superior Classification Model Constructed Using Metabolic Markers in Multiclass Metabolomics

Yang,

Chen,

Jiang

et al. 2024

Anal. Chem.

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Multiclass metabolomics has become a popular technique for revealing the mechanisms underlying certain physiological processes, different tumor types, or different therapeutic responses. In multiclass metabolomics, it is highly important to uncover the underlying biological information on biosamples by identifying the metabolic markers with the most associations and classifying the different sample classes. The classification problem of multiclass metabolomics is more difficult than that of the binary problem. To date, various methods exist for constructing classification models and identifying metabolic markers consisting of well-established techniques and newly emerging machine learning algorithms. However, how to construct a superior classification model using these methods remains unclear for a given multiclass metabolomic data set. Herein, MultiClassMetabo has been developed for constructing a superior classification model using metabolic markers identified in multiclass metabolomics. MultiClassMetabo can enable online services, including (a) identifying metabolic markers by marker identification methods, (b) constructing classification models by classification methods, and (c) performing a comprehensive assessment from multiple perspectives to construct a superior classification model for multiclass metabolomics. In summary, MultiClassMetabo is distinguished for its capability to construct a superior classification model using the most appropriate method through a comprehensive assessment, which makes it an important complement to other available tools in multiclass metabolomics. MultiClassMetabo can be accessed at http://idrblab.cn/ multiclassmetabo/.

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Emerging metabolomic tools to study cancer metastasis

Cited by 29 publications

References 126 publications

1H-NMR metabolomics analysis identifies hypoxanthine as a novel metastasis-associated metabolite in breast cancer

1H-NMR metabolomics analysis identifies hypoxanthine as a novel metastasis-associated metabolite in breast cancer

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

MultiClassMetabo: A Superior Classification Model Constructed Using Metabolic Markers in Multiclass Metabolomics

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