ABSTRACT
Ovarian cancer is the most lethal gynecological malignancy, owing to the fact that most cases are diagnosed at a late stage. To improve prognosis and reduce mortality, we must develop methods for the early diagnosis of ovarian cancer. A step towards early and non-invasive cancer diagnosis is through the utilization of extracellular vesicles (EVs), which are nanoscale, membrane-bound vesicles that contain proteins and genetic material reflective of their parent cell. Thus, EVs secreted by cancer cells can be thought of as cancer biomarkers. In this paper, we present gold nanohole arrays for the capture of ovarian cancer (OvCa)-derived EVs and their characterization by surface-enhanced Raman spectroscopy (SERS). For the first time, we have characterized EVs isolated from two established OvCa cell lines (OV-90, OVCAR3), two primary OvCa cell lines (EOC6, EOC18), and one human immortalized ovarian surface epithelial cell line (hIOSE) by SERS. We subsequently determined their main compositional differences by principal component analysis and were able to discriminate the groups by a logistic regression-based machine learning method with â¼99% accuracy, sensitivity, and specificity. The results presented here are a great step towards quick, facile, and non-invasive cancer diagnosis.
Subject(s)
Extracellular Vesicles , Ovarian Neoplasms , Apoptosis , Cell Line, Tumor , Female , Humans , Ovarian Neoplasms/diagnosis , Spectrum Analysis, RamanABSTRACT
Extracellular vesicles (EVs) are secreted by all cells into bodily fluids and play an important role in intercellular communication through the transfer of proteins and RNA. There is evidence that EVs specifically released from mesenchymal stromal cells (MSCs) are potent cell-free regenerative agents. However, for MSC EVs to be used in therapeutic practices, there must be a standardized and reproducible method for their characterization. The detection and characterization of EVs are a challenge due to their nanoscale size as well as their molecular heterogeneity. To address this challenge, we have fabricated gold nanohole arrays of varying sizes and shapes by electron beam lithography. These platforms have the dual purpose of trapping single EVs and enhancing their vibrational signature in surface-enhanced Raman spectroscopy (SERS). In this paper, we report SERS spectra for MSC EVs derived from pancreatic tissue (Panc-MSC) and bone marrow (BM-MSC). Using principal component analysis (PCA), we determined that the main compositional differences between these two groups are found at 1236, 761, and 1528 cm-1, corresponding to amide III, tryptophan, and an in-plane -C=C- vibration, respectively. We additionally explored several machine learning approaches to distinguish between BM- and Panc-MSC EVs and achieved 89 % accuracy, 89 % sensitivity, and 88 % specificity using logistic regression.
