A MOF-derived iron oxide nanorod platform for multiplexed detection of ovarian cancer extracellular vesicle biomarkers

High-grade serous ovarian cancer (HGSOC) is a highly aggressive malignancy often diagnosed at an advanced stage due to the absence of early symptoms and effective diagnostic tools. Extracellular vesicles (EVs) secreted by tumour cells carry disease-specific biomarkers, offering potential for early detection. However, their low concentration in biological samples poses challenges for isolation and detection, necessitating highly sensitive and specific multiplexed assays for subsequent detection of multiple biomarkers. Herein, we report the design of metal-organic framework (MOF)-derived porous superparamagnetic iron oxide nanorods (MOF-IONRs) to construct a rapid and sensitive surface-enhanced Raman scattering (SERS)-based multiplexed assay to detect HGSOC-specific EV protein biomarkers in clinical samples. The high porosity and large surface area of MOF-IONRs enable enhanced antibody loading and efficient biomarker capture, while simultaneously enriching SERS nanotags for signal amplification. Their intrinsic magnetic properties facilitate straightforward magnet-based isolation and purification of EVs. Additionally, the incorporation of mesoporous gold nanoparticle (mAuNP)-based SERS nanotags further enhance the Raman signal intensity. This integrated platform exhibits a limit of detection (LoD) of 2.13 EVs per µL with excellent reproducibility (%RSD < 10%, n = 3). Clinical validation successfully distinguishes ovarian cancer patients from healthy controls, highlighting its diagnostic accuracy and reliability. This multiplexed platform shows promise as a liquid biopsy for the early diagnosis of HGSOC, enabling rapid, cost-effective, and highly sensitive detection of EV-associated biomarkers in complex clinical samples. Moreover, integration with a handheld Raman spectrometer provides portability and compatibility with point-of-care (POC) testing, highlighting its promise as a transformative tool in ovarian cancer diagnostics and patient management.