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Metabolites
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Nutritional Metabolomics in Cancer
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Nutritional Metabolomics in Cancer

A special issue of Metabolites (ISSN 2218-1989). This special issue belongs to the section "Nutrition and Metabolism".

Deadline for manuscript submissions: closed (5 May 2025) | Viewed by 1963

Special Issue Editors

Dr. Hayato Muranaka

E-Mail Website
Guest Editor
Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
Interests: cancer; tumor microenvironment; metabolism; lipid; nutrients
Dr. Suguru Saito

E-Mail Website
Guest Editor
Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
Interests: immunology; anti-tumor immunity; cancer immunotherapy; metabolism; nutritional science

Special Issue Information

Dear Colleagues,

This Special Issue on “Nutritional Metabolomics in Cancer” in the journal Metabolites explores the intricate relationship between nutrition and cancer through a metabolomic approach. As our understanding of cancer biology advances, it becomes increasingly evident that diet and nutrition play pivotal roles in cancer development, progression, and therapy. This Special Issue complies cutting-edge research that utilizes metabolomics to reveal how dietary components influence cancer metabolism, identify metabolic biomarkers for cancer prognosis and treatment response, and elucidate metabolic pathways affected by nutritional interventions. We encourage submissions investigating metabolic alterations induced by specific diets, nutrients, or bioactive food compounds, and their implications in cancer biology. Studies exploring the interaction between nutritional status and cancer treatment efficacy, as well as those identifying metabolic signatures predictive of cancer outcomes, are particularly welcome. The scope includes mechanistic studies on dietary metabolites in cancer cell metabolism, the identification and validation of metabolomic biomarkers for early detection, prognosis, and therapeutic monitoring, and interventional studies examining the effects of specific diets or nutritional compounds on cancer metabolism. By highlighting innovative research, we aim to pave the way for integrating nutritional strategies into cancer prevention and treatment, ultimately contributing to personalized nutritional interventions that enhance cancer care. We invite researchers and clinicians to submit original research articles, reviews, and case studies aligning with the themes of this Special Issue.

Dr. Hayato Muranaka
Dr. Suguru Saito
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • nutritional metabolomics
  • cancer metabolism
  • nutritional interventions
  • metabolomics in cancer therapy
  • cancer biomarkers

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Published Papers (2 papers)

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13 pages, 5185 KiB  
Article
A Comprehensive Metabolomic and Microbial Analysis Following Dietary Amino Acid Reduction in Mice
by Raghad Khalid Al-Ishaq, Carmen R. Ferrara, Nisha Stephan, Jan Krumsiek, Karsten Suhre and David C. Montrose
Metabolites 2024, 14(12), 706; https://doi.org/10.3390/metabo14120706 - 14 Dec 2024
Viewed by 1322
Abstract
Introduction: Nutritional metabolomics provides a comprehensive overview of the biochemical processes that are induced by dietary intake through the measurement of metabolite profiles in biological samples. However, there is a lack of deep phenotypic analysis that shows how dietary interventions influence the metabolic [...] Read more.
Introduction: Nutritional metabolomics provides a comprehensive overview of the biochemical processes that are induced by dietary intake through the measurement of metabolite profiles in biological samples. However, there is a lack of deep phenotypic analysis that shows how dietary interventions influence the metabolic state across multiple physiologic sites. Dietary amino acids have emerged as important nutrients for physiology and pathophysiology given their ability to impact cell metabolism. Methods: The aim of the current study is to evaluate the effect of modulating amino acids in diet on the metabolome and microbiome of mice. Here, we report a comprehensive metabolite profiling across serum, liver, and feces, in addition to gut microbial analyses, following a reduction in either total dietary protein or diet-derived non-essential amino acids in mice. Results: We observed both distinct and overlapping patterns in the metabolic profile changes across the three sample types, with the strongest signals observed in liver and serum. Although amino acids and related molecules were the most commonly and strongly altered group of metabolites, additional small molecule changes included those related to glycolysis and the tricarboxylic acid cycle. Microbial profiling of feces showed significant differences in the abundance of select species across groups of mice. Conclusions: Our results demonstrate how changes in dietary amino acids influence the metabolic profiles across organ systems and the utility of metabolomic profiling for assessing diet-induced alterations in metabolism. Full article
(This article belongs to the Special Issue Nutritional Metabolomics in Cancer)
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Review

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29 pages, 959 KiB  
Review
Machine Learning-Driven Insights in Cancer Metabolomics: From Subtyping to Biomarker Discovery and Prognostic Modeling
by Amr Elguoshy, Hend Zedan and Suguru Saito
Metabolites 2025, 15(8), 514; https://doi.org/10.3390/metabo15080514 - 1 Aug 2025
Viewed by 199
Abstract
Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted [...] Read more.
Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies. Full article
(This article belongs to the Special Issue Nutritional Metabolomics in Cancer)
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