Dual-Functional -CN@AgNPs Nanosheets-Based Chemiluminescence Immunosensor for PD-L1-Expressing Exosomes and Classification of Lung Adenocarcinoma Subtypes.

Precise classification of lung adenocarcinoma (LUAD) subtypes is very important for determining surgical necessity and strategy. There is an urgent need to develop a noninvasive and accurate method to improve the accuracy of preoperative diagnosis. Programmed death-ligand-1 (PD-L1)-expressing exosomes are pivotal biomarkers for monitoring the LUAD progression. Nevertheless, most previously reported sensors are limited by sensitivity, which makes it challenging to differentiate LUAD subtypes. Herein, we synthesized silver nanoparticle-decorated graphitic carbon nitride (-CN@AgNPs) nanosheets dual-functionalized with 8-amino-5-chloro-2,3-dihydro-7-phenylpyrido-[3,4-]-pyridazine-1,4-dione (L012) and Co (GALC) via an in situ growth strategy. The composite exhibited a strong CL intensity and stability. This was attributed to the fact that -CN not only enriched a large amount of L012 but also catalyzed the generation of reactive OH together with Co, synergistically amplifying the CL signal. On this basis, a CL immunosensor for detecting PD-L1-expressing exosomes was constructed, using PD-L1 antibody-modified GALC as a CL immunoprobe and FeO@TiO nanoparticle as a capture platform for exosomes. The optimized immunosensor exhibited exceptional sensitivity, with a low limit of detection of 28.1 particles/mL and a wide linear range of 4.28 × 10-4.28 × 10 particles/mL, outperforming previously reported sensors. Clinical evaluation using 50 serum samples revealed statistically significant differences in PD-L1-expressing exosome levels among healthy individuals, minimally invasive adenocarcinoma patients, and invasive adenocarcinoma patients ( < 0.0001), with excellent discriminatory ability (AUC = 0.947). This work not only achieves the ultrasensitive detection of PD-L1-expressing exosomes at the individual level but also presents a facile, noninvasive, and accurate method for LUAD diagnosis and subtype classification.