Integrating Nutritional Epigenomics in Oncology for Biomarker Discovery and Therapeutics

In recent years, the field of oncology has increasingly acknowledged cancer as a metabolic disorder with significant nutritional determinants. The advent of multi-omics technologies and artificial intelligence (AI), particularly machine learning (ML), has led to the emergence of integrative frameworks that facilitate the understanding of the complex relationships between diet, metabolism, and cancer processes. Despite these advances, the precise mechanisms through which nutrition influences cancer biology remain insufficiently explored, particularly at the level of epigenetic modifications and their impact on gene expression and tumor development. Current research suggests a pivotal role for nutritional epigenomics in elucidating these complex interactions. Though several significant studies have highlighted the potential of this line of inquiry, there exists a critical need for more comprehensive exploration of nutritional epigenetics in oncology and validation in diverse populations and cancer types.

This Research Topic aims to systematically compile evidence on how multi-omics and AI/ML methodologies can enhance our understanding of the nutrition-metabolism-epigenomics-cancer axis. The primary objective is to explore the potential of these technologies in identifying novel biomarkers and therapeutic targets in this axis that could be effectively translated into real-world oncology practices. In doing so, we seek to address specific questions regarding the utility and efficacy of nutritional strategies in cancer treatment and prevention, evaluating their impact on patient outcomes across different healthcare systems, particularly in developing countries. Through this endeavor, we hope to facilitate the identification of novel biomarkers and therapeutic strategies that could revolutionize personalized oncology adjunct treatments through nutrition.

To gather further insights into the role of nutritional epigenomics in cancer research, we welcome articles addressing, but not limited to, the following themes:

- Integration of multi-omics and AI/ML approaches in oncology in understanding the nutrition- epigenomics-cancer axis.
- Role of diet in cancer epigenomics and metabolism.
- Discovery of nutritional biomarkers for cancer detection and prognosis.
- Translational potential of nutritional interventions in cancer therapy.
- Comparative analyses of nutritional epigenomics within diverse healthcare systems.


Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases that are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) will not be accepted in any of the sections of Frontiers in Oncology.

Dr. Hiroshi Ureshino declared honoraria from Novartis.
Dr. Jyotdeep Kaur, Dr. Alejandro Schcolnik‐Cabrera, and Dr. Andrea Nicolini declared no conflicts of interest.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Case Report
• Clinical Trial
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• ... View all formats

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Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Case Report
• Clinical Trial
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: Biomarker Discovery, Nutritional Epigenomics, Oncology Therapeutics

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.