Combining serum albumin via adsorption-exfoliation on hydroxyapatite particles (HAp) with surface-enhanced Raman scattering (SERS), we developed a novel quantitative analysis of albumin method from blood serum for breast cancer screening applications. For adults, the normal range of serum albumin is defined as 3.5-5.0 g/dL, and the levels <3.5 g/dL is called hypoalbuminemia. The quantitatively analysis obtained by our HAp method had a good linear relationship from 1 to 10 g/dL. More importantly, the lower limit of detection was less than the albumin prognostic factor for disease (3.5 g/dL). Serum albumin was adsorbed and exfoliated by HAp from serum samples of breast cancer patients and healthy volunteers, and then mixed with silver colloids to perform SERS spectral analysis. Subtle changes in the SERS spectra of serum proteins indicated that some specific biomolecular contents and albumin secondary structures change with cancer progression. Principal component analysis (PCA), as a spectral dimensionality reduction method, combining with a linear discriminant analysis (LDA) was employed to screen and classify breast cancer. Based on the PCA-LDA algorithm, yielding the diagnostic sensitivity and specificity of breast cancer patients were 95% and 90%, respectively. This exploratory work demonstrated that HAp adsorbed-exfoliated serum proteins combined with SERS spectroscopy has great potential for label-free and non-invasive screening of breast cancer.
Esophageal carcinoma is a common cancer worldwide with a high mortality. Early diagnosis and treatment is critical to reduce the mortality of esophageal cancer patients. In this work, we developed a novel method for detection of esophageal cancer by Raman spectroscopy measurements of extracellular fluid taken from esophageal tissue. The extracellular fluid samples were prepared by sliding the esophageal tissue over an aluminum plate substrate, and then the Raman spectra of the air-drying extracellular fluid samples from 10 esophageal cancer patients and 10 healthy volunteers were successfully recorded. Difference spectrum analysis combined with the assignment of Raman bands indicated that there were subtle but distinct changes between esophageal cancer and normal tissues, which could be associated with the special changes of nucleic acid, protein, lipid and other biological molecules during the process of canceration. To further investigate the diagnostic ability of extracellular fluid taken from human esophageal tissue, the spectral data was combined with multivariate analysis processes. Principal component analysis (PCA), as a spectral dimensionality reduction approach, and in conjunction with the linear discriminant analysis (LDA) algorithm, was employed to identify the esophageal cancer samples, and the diagnostic sensitivity and specificity of 90% and 80%, respectively, could be achieved for classification between normal and cancer groups. Moreover, receiver operating characteristic (ROC) curves further confirmed the effectiveness of the diagnostic algorithm based on PCA-LDA diagnostic algorithm. The results of this exploratory study demonstrated the great potential of esophageal cancer screening based on the analysis of extracellular fluid of tissue, and provided a rapid and label-free tool for clinical cancer detection.
During the development of tumors, some protein molecules, secreted proteins, are secreted, which are closely related to the proliferation, invasion and metastasis of malignant tumor cells. Therefore, the study of tumor cell secreted proteins not only helps to understand the molecular mechanism of tumorigenesis and development, but also helps to find new tumor markers for early screening of cancer and monitoring of high-risk populations. Surface-enhanced Raman spectroscopy (SERS) and partial least squares-support vector machine (PLS-SVM) data processing methods were used to characterize secreted proteins from human liver cancer cells HepG2 and normal human liver cells LO2 cells in this paper. The discriminative sensitivity and specificity of secreted proteins reach 100%, respectively. These results show that SERS technology combined with PLS-SVM data processing method can effectively distinguish normal cells from cancer cells and provide new ideas for finding biomarkers of cancer cells.
Mast cell (MCs) researches have received worldwide attention and achieved great achievements. Degranulation of MCs is not only related to anaphylaxis, but also plays an important role in the formation and progression of tumor. The existing detection methods could not fully reflect the degree of cell degranulation. In this paper, surface-enhanced Raman scattering (SERS) was used to detect and analyze the degranulation degree of MCs treated with different concentrations of C48/80 (compound 48/80, a mast cell activator). The culture supernatants of cells treated with different concentrations of C48/80 (0 μg/mL, 2 μg/mL and 10 μg/mL) were mixed with Ag colloids and high quality SERS spectra were acquired. The assignment of SERS bands combined with differential spectra analysis indicated that biomolecules associated with cell degranulation in the C48/80 treated groups were changed compared with the control group, including a decrease in the percentage of lipid content and an increase in the relative contents of collegen and phosphatidylserine. Furthermore, principal component analysis (PCA) and linear discriminant analysis (LDA) diagnostic algorithms were employed to analyze and distinguish the SERS spectra of different cell degranulation groups with high sensitivity, specificity and accuracy. The larger value of the integration area under the ROC curve also suggested the greater forecast accuracy. This exploratory work demonstrates that the combination of SERS technology and PCA-LDA algorithm has great potential for developing a label-free, comprehensive and accurate method for detecting cell degranulation.
In this article, we have studied the feasibility of using Raman spectroscopy and multivariate statistical algorithms to distinguish human hepatoma cells from normal human liver cells with the aim to explore a label-free and non-invasive method for detecting and screening hepatoma cells. High-quality Raman spectra were obtained from 50 normal liver cells (Lo2 cell line) and 50 hepatoma cells (HepG2 cell line) in the range of 500-1750 cm-1. There are significant differences in Raman spectra between normal liver cells and hepatoma cells, which indicated special changes in the content of biomolecules including nucleic acids, proteins and lipid in different cell lines. Principal component analysis (PCA) and linear discriminate analysis (LDA) were used to classify the Raman spectra obtained from hepatoma cells and normal liver cells, and the discrimination sensitivity and specificity were 98% and 100%, respectively. In addition, PCA in conjunction with support vector machine (SVM) (with a Gaussian radial basis function) was also employed to classify the same Raman spectra dataset, and the sensitivity and specificity could be improved to 100% and 100%, respectively, indicating that the classification performance of PCA-SVM is superior to that of PCA-LDA. This exploratory study demonstrated that Raman spectroscopy technique combined with multivariate statistical algorithms as a clinical cell-based biosensor has great potential for noninvasive cancer cell detection and screening.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.