Application of surface enhanced Raman spectroscopy as a diagnostic system for hypersialylated metastatic cancers. (July 2017) - Shashni, Babita
Abstract: Early diagnosis of metastatic cancers could greatly limit the number of cancer-associated deaths. Aberrant surface expression of sialic acid (hypersialylation) on tumors correlating with metastatic incidence and its involvement in tumorigenesis and progression is widely reported; hence detection of hypersialylated tumors may be an effective strategy to identify metastatic cancers. We herein report on the application of phenylboronic acid-installed PEGylated gold nanoparticles coupled with Toluidine blue O (T/BA-GNPs) as SERS probes to target surface sialic acid ( N -acetylneuraminic acid, Neu5Ac). Strong SERS signals from metastatic cancer cell lines (breast cancer; MDA-MB231 and colon cancer; Colon-26) were observed, contrary to non-metastatic MCF-7 cells (breast cancer). The detected SERS signals from various cancer cell lines correlated with their reported metastatic potential, implying that our T/BA-GNP based SERS system was capable of distinguishing the metastaticity of cells based on the surface Neu5Ac density. T/BA-GNP based SERS system could also significantly differentiate between hypersialylated tumor tissues and healthy tissues with high SERS signal to noise ratio, due to plasmon coupling between the specifically aggregated functionalized GNPs. Furthermore, we also confirmed reduction in SERS signals from MDA-MB231 surface upon treatment with our original reactive oxygen species (ROS)-scavenging polymeric micelle, nitroxide-radical containing nanoparticles (RNPs). The ROS-mediated abrogation of sialylation by impairing the activation of NF-κB-sialyltransferase signaling cascade upon RNP treatment was confirmed by expression studies and the T/BA-GNPs based SERS system. The aforementioned findings thus, establish T/BA-GNPs based SERS as a potential cytodiagnostic system to detect hypersialylated metastatic tumors and RNPs as anti-metastatic cancer drug candidates. Graphical abstract: