Reversing cancer cell behavior using AI-guided CRISPR and quantum nanobiology: a systems-based approach to epigenetic reprogramming.

Treatment effectiveness is hindered by the phenotypic plasticity of cancer and the genetic complexity of tumors. However, CRISPR-Cas-based medicines face challenges with specificity, off-target effects, and tumor heterogeneity adaptability. This work investigates the possible combination of quantum biological processes, artificial intelligence, and nanomaterials to improve CRISPR gene editing and modulate or reverse selected malignant phenotypes. Quantum machine learning (QML) can be used to simulate quantum processes like electron tunneling in DNA repair and spin-dependent enzyme activity. To enable exact tumor phenotypic reversal, these models will be combined with optimization approaches powered by AI to direct CRISPR editing in oncogenic signaling networks. Graphene, gold nanoparticles, and lipid-based vectors are some of the nanomaterials that will be used as carriers to effectively and deliver CRISPR systems in a biocompatible manner to the cancer microenvironment. We hypothesize that selected homeostatic gene-expression states may be partially restored in experimental cancer models through the integration of quantum-informed AI, CRISPR gene alteration, and nanomaterial delivery. This integrated strategy could support future cancer therapies that move beyond tumor suppression toward controlled modulation of malignant cell states, although substantial preclinical and clinical validation remains necessary.