Artificial intelligence-assisted spatial omics-based biomimetic nanoplatform for intelligent and precise intervention in the immunosuppressive core region of ovarian cancer

Abstract Ovarian cancer (OC) ranks among the most aggressive malignancies of the female reproductive system. The immunosuppressive tumor microenvironment (TME) and pronounced spatial heterogeneity significantly restrict the efficacy of immunotherapy. Recent advances in single-cell omics and spatial transcriptomics (ST) have enabled the identification of immunosuppressive core regions within the TME at molecular and spatial levels. These regions often contain “exclusion structures” composed of regulatory T cells (Tregs), tumor-associated macrophages (TAMs), and myeloid-derived suppressor cells (MDSCs) in hypoxia-enriched niches. To achieve precise therapeutic modulation of these core microregions, researchers have developed biomimetic nanodrug platforms such as cell membrane-coated systems and exosome-based carriers. These platforms deliver immunoregulatory agents targeting programmed death-ligand 1 (PD-L1), transforming growth factor-beta (TGF-β), and colony-stimulating factor 1 receptor (CSF1R), with enhanced efficiency and adaptability through multi-responsive mechanisms. The paper provides a comprehensive review of the spatial organization mechanisms, omics-based identification methods, and signaling network pathways that define the immunosuppressive core regions in OC. It also summarizes the design principles and adaptive strategies of biomimetic platforms and introduces an artificial intelligence (AI)-assisted closed-loop therapeutic framework encompassing identification, delivery, and feedback to support individualized and precise immunotherapy. Although the approach demonstrates strong potential for multidimensional integration, several challenges remain, including the standardization of spatial atlases, the stability of delivery systems, and cross-platform compatibility, all of which require further technological advancement.