LAUSR

Simple Summary Although various malignant tumors express high levels of lysyl oxidase (LOX) and though its role in tumor progression is well-defined, there is a lack of sensing techniques to target LOX. This study highlights the application of peptide-functionalized gold nanoprobes for sensing the LOX levels in tumor microenvironments. The gold nanoparticles (AuNPs) in these nanoprobes aggregate upon exposure to LOX, resulting in a red shift of the surface plasmon resonance peak, accompanied by a characteristic color change. This colorimetric assay based on peptide-functionalized AuNP sensitively detects LOX secreted from various cancer cells not only in vitro but also in the tissue extract. In this study, the suggested analytical approach demonstrated high specificity to LOX and did not show any color change in the presence of other enzymes. Abstract High LOX levels in the tumor microenvironment causes the cross-linking of extracellular matrix components and increases the stiffness of tumor tissue. Thus, LOX plays an important role in tumorigenesis and in lowering the tumor response to anticancer drugs. Despite comprehensive efforts to identify the roles of LOX in the tumor microenvironment, sensitive and accurate detection methods have not yet been established. Here, we suggest the use of gold nanoparticles functionalized with LOX-sensitive peptides (LS-AuNPs) that aggregate upon exposure to LOX, resulting in a visual color change. LOX-sensitive peptides (LS-peptides) contain lysine residues that are converted to allysine in the presence of LOX, which is highly reactive and binds to adjacent allysine, resulting in the aggregation of the AuNPs. We demonstrated that the synthesized LS-AuNPs are capable of detecting LOX sensitively, specifically both in vitro and in the tissue extract. Moreover, the suggested LS-AuNP-based assay is more sensitive than commonly employed assays or commercially available kits. Therefore, the LS-AuNPs developed in this study can be used to detect LOX levels and can be further used to predict the stiffness or the anticancer drug resistance of the tumor.