Visual dual-mode aptasensor for non-small cell lung cancer exosome detection via HRP self-coupling enhanced oxidized iridium nanoparticle aggregation

Abstract

The main reason for the high mortality rate of non-small cell lung cancer is that patients are usually diagnosed at an advanced stage of the disease. Exosomes, small membrane vesicles secreted by normal cells or tumor cells, play a significant role in the progression of NSCLC. This study successfully optimized the preparation of artificial nanoenzymes self-coupling with horseradish peroxidase (IrO₂NPs@HRP-AptCD63), without adding any ligand, demonstrating remarkable catalytic activity suitable for detecting the EGFR protein on the surface of NSCLC exosomes. When fused with the CD63 aptamer for identifying NSCLC exosomes, IrO₂NPs@HRP showed enhanced catalytic activity in the 3,3′,5,5′-tetramethylbenzidine-H₂O₂ oxidation–reduction system, thereby enhancing the colorimetric signal. This phenomenon can be distinguished by the naked eye and quantified using a UV–Vis spectrophotometer. Meanwhile, as the redox reaction occurs, the current signal of 3,3′,5,5′-tetramethylbenzidine-H₂O₂, acting as an electrolyte, changes. The developed aptasensor generates dual-mode signal outputs, firstly, to visually assess the samples for their positive or negative status, and subsequently employ more in-depth electrochemical or colorimetric analysis methods for a detailed quantitative analysis of suspected positive samples. The detection limits of electrochemical analysis and colorimetric analysis were 0.9 × 10³ particles/mL and 0.14 × 10³ particles/mL, respectively. Compared with traditional biomarkers such as CA125, this method exhibits exceptional specificity, capable of simultaneously distinguishing serum exosomes of healthy volunteers, COPD patients, and NSCLC patients, promoting exosome detection in mouse models for tumor monitoring. Additionally, it elucidates the changes in EGFR protein expression on the surface of serum exosomes throughout the developmental trajectory.

Graphical abstract