Abstract
Kidney cancer (KC) is a significant threat to human life and suffers high postoperative recurrence. So far, it still lacks a rapid method for diagnosis and monitoring. Tumor-derived small extracellular vesicles (sEVs) with high heterogeneity in human serum are potential biomarkers for convenient KC detection. A label-free plasmonic metasurface biosensor could provide a tool to transduce the nanoscale sEV binding event into the measurable optical response. However, the label-free meta-plasmonic biosensing has the shortage of biophysical demonstration fully addressing the heterogeneity quantification of tumor-derived sEVs, which limits the establishment of standardized metrics for fast sEV profiling of KC. In this work, we develop a volume-based biophysical model that quantitatively links meta-plasmonic signal changes to the broad-size distribution variations of KC-derived sEVs in serum samples. Our theoretical framework focuses on the fundamental meta-plasmonic mechanism of accumulating sEV diversity rather than specific metasurface design details, providing a rigorous mathematical basis to interpret sEV-induced spectral shifts in terms of vesicle quantity and size heterogeneity. By unlocking the metrics of tumor biomarker heterogeneity, our model-guided meta-plasmonic biosensing strategy accomplishes the prominent limit of sEV detection as low as 141 particle/mL, with high membrane protein specificity. Our sEV metrical methodology supports the building the system of portable testing within 20 min, enhancing high-sensitivity diagnosis of early KC with the area under the curve up to 90 % and enabling 100 % accuracy for timely postoperative monitoring of KC recurrence.
•A volume-based model supports label-free metasensing of extracellular vesicle heterogeneity powerfully.•The meta-plasmonic biosensing achieves the limit of detection as low as 141 particle/mL.•The biosensors facilitate high-sensitivity diagnosis of early kidney cancer, with the AUC up to 90 %.•The portable biosensing shows 100 % accuracy for timely postoperative monitoring of kidney cancer recurrence.