Integrating AI and RNA biomarkers in cancer: advances in diagnostics and targeted therapies

Early detection and personalized treatment strategies are essential for enhancing patient outcomes, as cancer continues to be a significant cause of mortality on a global basis. In clinical practice, the identification and validation of reliable biomarkers for cancer diagnosis, prognosis, and therapeutic monitoring continue to present significant challenges. The present study explores the current state and applications of artificial intelligence-driven approaches in the identification and usage of RNA biomarkers for cancer diagnostics and therapeutics. In various aspects of cancer management, we explore the integration of machine learning and deep learning algorithms with a variety of RNA biomarker classes, such as circRNAs, miRNAs, and lncRNAs. Improved detection, subtype categorization, prognosis prediction, and treatment response monitoring are all possible due to AI-powered approaches that can efficiently analyse complex RNA expression patterns, discover novel biomarkers, and explain their functions in cancer biology. There are still many obstacles to overcome in the biomarker development, validation, and clinical application processes, despite the fact that RNA biomarkers hold great potential to transform cancer treatment by improving early detection and individualized therapy methods. Integrating AI with RNA biomarker research is a crucial strategy with enormous promise for precision oncology and better patient care all the way through the cancer spectrum, from risk prediction to recurrence management.