Emerging roles of artificial intelligence/machine learning (AI/ML) towards new understandings in molecular crosstalk between circRNA–CUL3–TKI to resensitize chemoresistant cancers

Feb 1, 2026Molecular and cellular probes

How Artificial Intelligence Helps Reveal Molecular Interactions That May Reverse Drug Resistance in Cancer

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Longevity & Aging on OpenScience ↗PubMed ↗DOI ↗

Abstract

An emerging circRNA-CUL3-TKI regulatory framework may contribute to oncogenesis and drug sensitivity.

CircRNAs and Cullin-3 are implicated in cancer modulation and therapy resistance.
CircRNA networks could influence pathways related to autophagy, ferroptosis, stress responses, and redox signaling.
Exosomal circRNAs and circRNAs from the CUL3 gene may act as mediators of resistance and potential biomarkers.
Interactions with KEAP1-NRF2 signaling are associated with enhanced tumor survival during therapy.
The circRNA-CUL3-TKI axis may serve as a framework for testing new therapeutic strategies.
Applications of artificial intelligence and machine learning could aid in modeling circRNA networks and predicting TKI responses.

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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.

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Emerging roles of artificial intelligence/machine learning (AI/ML) towards new understandings in molecular crosstalk between circRNA–CUL3–TKI to resensitize chemoresistant cancers

Abstract

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