Decoding exercise impacts on cancer using computational and omics technologies

Exercise has been shown to offer significant benefits in improving cancer prognosis, yet the precise molecular mechanisms driving these effects remain largely unexplored. The systemic response to physical activity encompasses a complex array of interactions across genomic, transcriptomic, proteomic, and metabolomic levels. This vast, multi-dimensional biological challenge is now being addressed through high-throughput omics technologies, uncovering previously hidden molecular patterns. Despite advancements, extracting biologically meaningful insights and establishing causal mechanisms from these comprehensive datasets requires advanced bioinformatic and machine learning methodologies.

This Research Topic aims to utilize integrated multi-omics data alongside machine learning strategies to systematically explore the molecular mechanisms and therapeutic targets influenced by exercise in cancer prognosis. We encourage research that develops computational models to identify reliable biomarkers for patient stratification and pathway analysis. The ultimate goal is to facilitate the development of personalized exercise regimens that are supported by molecular evidence, thereby linking data-driven discoveries with clinical applications in oncology.

To gather further insights in this complex field, we welcome articles addressing, but not limited to, the following themes:

o Application of machine learning and artificial intelligence to integrate multi-omics data from pre-clinical or clinical exercise studies in cancer.

o Identification of exercise-induced biomarkers predictive of cancer risk, treatment response, or survival outcomes.

o Elucidation of biological mechanisms and novel molecular targets, such as myokines and oncometabolites, underlying the exercise-cancer interaction.

o Development of computational models and frameworks for translating exercise-responsive signatures into actionable clinical insights.

o Studies addressing the challenges of data integration, model interpretability, and validation in this context.