Identification of breast cancer through spectroscopic analysis of cell-membrane sialic acid expression.

Identification of specific and reliable biomarkers or unique characteristics is significant for cancer molecular diagnosis and cancer therapeutic assessment. As a biomarker, sialic acid expression in human biofluid or on cell surface is one of interest to determine the tumor malignancy and metastasis since it involves in many crucial metabolic processes. In this work, we aimed to develop a molecular diagnosis method to make it possible to distinguish human breast cancer and normal tissues by capturing rich spectral features of phenyboronic acid-based nanoprobe (MPBA@AgNP) when it binds to sialic acid on cell surface. We analyzed and found that the marker bands at 1074 and 1570 cm-1 recorded by Surface-enhanced Raman spectroscopy (SERS) displayed discernible spectral differences in vitro cell lines. Density functional theory (DFT) was further used to explore and support the detailed changes of vibrational modes affected by sialic acid. This method is generally applicable by testing three types of in vitro cell lines (HepG2, Hela, BNL.CL2) and one pair of the tissue sections (cancer tissue and normal tissue) from the human breast regions. Besides, the area under receiver operating characteristic (ROC) curves for 1074 and 1572 cm-1 are 0.9419 and 0.9023, confirming determination of the specific molecular expression by the spectral features holds potential promise for improving cancer detection accuracy. Furthermore, sialic acid expression and distribution acquired of breast tissues by confocal SERS mapping further indicated our method is possible for cancer early diagnosis and toward to real-time in vivo study.