AI-Driven Nutrigenomics: Targeting Mitochondrial Metabolism and Growth Factor Signaling in Translational Oncology
Abstract
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.
Aim:: This review aims to synthesize the molecular cross-talk between dietary bioactives and the mitochondrial-GFS axis, and proposes, as a conceptual framework informed by computational approaches, a 'Triple-Hit' framework to overcome drug resistance.Main Body: The roles of the PI3K/Akt/mTOR and IGF-1 axes in regulating glycolytic commitment are discussed, together with evidence that bioactives such as Quercetin and EGCG can modulate mitochondrial fission/fusion proteins (Drp1/Mfn2) and influence apoptotic sensitivity in preclinical models. Furthermore, mito-epigenetics is discussed, including growth factor-mediated hypermethylation of the mitochondrial D-loop and its potential reversal by nutrigenomic HDAC and DNMT inhibitors.…
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