SERS Studies On Normal Epithelial And Cancer Cells Derived From Clinical Breast Cancer Specimens

Objective:We aimed to distinguish the SERS spectral characteristics of primary breast cancer single cell from those of primary normal epithelial single cell and to construct diagnostic models with random forest.Methods:Our study obtained single-cell suspensions from breast cancer and normal breast tissue samples from the same patients by a mechanical enzymatic digestion method,and the CD326+/CD45-cells in the suspensions were selected by flow cytometry.Surface-enhanced Raman scattering(SERS)spectroscopy of single-cell suspensions was obtained and analyzed.Random forest classification was implemented to develop effective diagnostic algorithms for the classification of SERS of different typed cells.Result:We first examined the SERS spectra of the primary breast cancer single cell and normal epithelial single cell obtained by flow sorting cytometry due to their biomarkers of CD326+/CD45-.Comparison analyses on their SERS spectra disclose that the standard deviation(SD)of the normal epithelial single cell is larger than that of the breast cancer cell,and the SD of the primary breast cancer single cell is more obvious than that of the normal epithelial cells.The SERS band shifts and the difference spectrum of two kinds of single cell indicated that protein,the nucleic acid,the cholesterol,palmitic acid,and sphingomyelin in the cancer cell profiles exhibit stronger than those of normal cells,while the monounsaturated fatty acids and triglycerides in cancer cells are significantly lower than those in normal cells.However the stability of lipid chains has been reduced in tumor tissue.In addition,the prospective application of an algorithm to the dataset results in an accuracy of 78.2%,a precision of 75.5%,and a recall of 66.7%.Conclusion:We aimed to distinguish the SERS spectral characteristics of primary breast cancer single cell from those of primary normal epithelial single cell and to construct diagnostic models with the SERS spectra of primary single cell.The diagnostic model created by machine learning can identify subtle changes in SERS.The results of this experiment will help us improve the diagnostic technology for breast cancer using SERS spectroscopy to improve the rate of breast conservation and the survival rate of breast cancer patients and reduce the rate of secondary surgery.