Title : Harnessing big data and Artificial Intelligence to uncover a universal principle for cancer elimination
Abstract:
Although numerous cancer-associated mechanisms are reported each year, a unifying theoretical framework capable of explaining and ultimately enabling the elimination of all cancers remains elusive, highlighting fundamental limitations in current cancer research. These limitations stem from experimental and computational biases inherent in conventional approaches, hindering the discovery of universal biological principles. Advances in big data analytics and Artificial Intelligence (AI) offer new opportunities to overcome these biases through large-scale, data-driven investigations. Recent studies leveraging the world’s largest genomic and transcriptomic datasets reveal that noncoding RNAs (ncRNAs) constitute a functional system distinct from the protein-centered system and serve as central, potentially universal regulators of oncogenesis, challenging the long-standing protein-centric paradigm. This emerging perspective creates unprecedented opportunities to accelerate cancer research and therapeutic development; however, major challenges remain. The functional architecture and structural properties of ncRNAs are still poorly understood, and traditional conceptual frameworks and commonly used mouse models do not adequately capture the complexity and species-specific characteristics of human ncRNA biology. Furthermore, unlike proteins, ncRNAs often follow dynamic, context-dependent evolutionary trajectories rather than fixed sequence conservation patterns, limiting the reliability of conventional pattern-recognition and comparative approaches. Consequently, the lack of a comprehensive theoretical and computational framework for studying ncRNA systems remains a major obstacle to translating these discoveries into broad clinical applications. Overcoming these barriers will require transformative conceptual models, innovative computational strategies, and next-generation technological platforms that integrate computational biology, large-scale multi-omics data, and artificial intelligence to uncover the fundamental principles of ncRNA function and accelerate the development of universal strategies for cancer prevention, diagnosis, and treatment.
