Decoding epigenetic signatures of extracellular RNA in ovarian cancer through artificial intelligence

Artificial intelligence (AI) is redefining the interpretation of epigenetic regulation in RNA biology by enabling the integration of complex, multi-layered datasets spanning genomic, epigenomic, and transcriptomic levels. These improvements are relevant for non-coding RNA associated with extracellular vesicles (EV-ncRNA), whose content may offer a minimally invasive readout of the molecular programs defining cancer cell states. In cancer, epigenetic modifications affect RNA editing, epi-transcriptomic changes, alternative splicing, and circular RNA (circRNA) biogenesis, also modulating the EV-ncRNA content. AI models can propose regulatory activity from sequence context and to integrate chromatin accessibility. These advances offer an opportunity to associate regulatory programs with EV-ncRNA signatures, moving beyond descriptive profiling toward biologically interpretable models. AI can then integrate multimodal data and apply these models to deduce specific cancer cell states from EV-ncRNA obtained by liquid biopsy. This perspective outlines a conceptual framework for understanding EV-ncRNA signatures in ovarian cancer (OC) and for exploring their potential roles in biomarker discovery, patient stratification, and dynamic disease monitoring. The major challenges that may delay effective implementation are also discussed, including data heterogeneity, the absence of standardized EV multi-omics datasets, and the limited interpretability of deep learning models.