Cancer arises and is resistant to therapy via intricate molecular networks that are poorly characterised. While individually, Cullin-3 (CUL3) and circular RNAs (circRNAs) have been reported to modulate cancer, their synergistic effect in the modulation of tyrosine kinase inhibitor (TKI) resistance is yet to be studied. An emerging circRNA-CUL3-TKI regulatory framework is highlighted as a potential contributor to oncogenesis and drug sensitivity in this review. We discuss how circRNA-associated networks may influence CUL3-dependent pathways implicated in tumour resistance to therapy by modulating autophagy, ferroptosis, stress-responses, and redox signalling. Exosomal circRNAs and circRNAs of the CUL3 gene itself are highlighted as dynamic mediators of resistance as well as biomarkers. How they interact with Kelch-like ECH-associated protein 1- Nuclear factor erythroid 2-related factor 2 (KEAP1-NRF2) signalling reveals that they enhance tumour survival under therapy pressure. By highlighting key processes of carcinogenesis and resistance, the circRNA-CUL3-TKI axis represents a testable therapeutic framework. Modeling circRNA networks, predicting TKI response, finding biomarkers, and developing personalised treatment plans are all made possible by applications of artificial intelligence and machine learning (AI/ML), as explored in this review. Antisense oligonucleotides, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-based molecules, neddylation inhibitors or PROteolysis TArgeting Chimera (PROTACs) are examples of potential interventions that, when combined with AI/ML techniques, improve therapeutic efficacy and may inform future desensitisation strategies. These collectively emphasize the emerging applications for AI/ML in understanding the circRNA-CUL3-TKI crosstalk and developing methods to resensitize cancers that are resistant to therapy.
Sudandiradoss et al. (Fri,) studied this question.