AI-Driven Nutrigenomics: Targeting Mitochondrial Metabolism and Growth Factor Signaling in Translational Oncology

Background

The metabolic flexibility of cancer cells, primarily driven by mitochondrial reprogramming and constitutive Growth Factor Signaling (GFS), remains a formidable barrier to therapeutic success. The complexity of metabolic hijacking requires advanced computational tools, such as artificial intelligence (AI), to analyze large-scale metabolic datasets and identify actionable vulnerabilities. While the "Warburg Effect" was traditionally viewed as a consequence of irreversible mitochondrial defects, modern evidence suggests a proactive "metabolic hijacking" where growth factors drive mitochondrial plasticity to support rapid biomass accumulation, although this model remains under active investigation and is not yet universally established.