Duo Lin

Colorectal cancer detection by gold nanoparticle based surface-enhanced Raman spectroscopy of blood serum and statistical analysis

The capabilities of using gold nanoparticle based surface-enhanced Raman spectroscopy (SERS) to obtain blood serum biochemical information for non-invasive colorectal cancer detection were presented in this paper. SERS measurements were performed on two groups of blood serum samples: one group from patients (n = 38) with pathologically confirmed colorectal cancer and the other group from healthy volunteers (control subjects, n = 45). Tentative assignments of the Raman bands in the measured SERS spectra suggested interesting cancer specific biomolecular changes, including an increase in the relative amounts of nucleic acid, a decrease in the percentage of saccharide and proteins contents in the blood serum of colorectal cancer patients as compared to that of healthy subjects. Both empirical approach and multivariate statistical techniques, including principal components analysis (PCA) and linear discriminant analysis (LDA) were employed to develop effective diagnostic algorithms for classification of SERS spectra between normal and colorectal cancer serum. The empirical diagnostic algorithm based on the ratio of the SERS peak intensity at 725 cm(-1) for adenine to the peak intensity at 638 cm(-1) for tyrosine achieved a diagnostic sensitivity of 68.4% and specificity of 95.6%, whereas the diagnostic algorithms based on PCA-LDA yielded a diagnostic sensitivity of 97.4% and specificity of 100% for separating cancerous samples from normal samples. Receiver operating characteristic (ROC) curves further confirmed the effectiveness of the diagnostic algorithm based on PCA-LDA technique. The results from this exploratory study demonstrated that gold nanoparticle based SERS serum analysis combined with PCA-LDA has tremendous potential for the non-invasive detection of colorectal cancers.

Label-free blood plasma test based on surface-enhanced Raman scattering for tumor stages detection in nasopharyngeal cancer

This study aims to evaluate the feasibility of a label-free nanobiosensor based on blood plasma surface-enhanced Raman spectroscopy (SERS) method for exploring variability of different tumor (T) stages in nasopharyngeal cancer (NPC). Au nanoparticles as the SERS-active nanostructures were directly mixed with human blood plasma to enhance the Raman scattering signals. High quality SERS spectra can be acquired from blood plasma samples belong to 60 healthy volunteers, 25 NPC patients with T1 stage and 75 NPC patients with T2–T4 stage. A diagnostic accuracy of 83.5% and 93.3%, respectively, can be achieved for classification between early T (T1) stage cancer and normal; and advanced T (T2–T4) stage cancer and normal blood groups. This exploratory study demonstrates that the nanobiosensor based on SERS technique in conjunction with PCA-LDA has great potential as a clinical complement for different T stages detection in nasopharyngeal cancer.

Study on gastric cancer blood plasma based on surface-enhanced Raman spectroscopy combined with multivariate analysis

A surface-enhanced Raman spectroscopy (SERS) method combined with multivariate analysis was developed for non-invasive gastric cancer detection. SERS measurements were performed on two groups of blood plasma samples: one group from 32 gastric patients and the other group from 33 healthy volunteers. Tentative assignments of the Raman bands in the measured SERS spectra suggest interesting cancer-specific biomolecular changes, including an increase in the relative amounts of nucleic acid, collagen, phospholipids and phenylalanine and a decrease in the percentage of amino acids and saccharide in the blood plasma of gastric cancer patients as compared with those of healthy subjects. Principal components analysis (PCA) and linear discriminant analysis (LDA) were employed to develop effective diagnostic algorithms for classification of SERS spectra between normal and cancer plasma with high sensitivity (79.5%) and specificity (91%). A receiver operating characteristic (ROC) curve was employed to assess the accuracy of diagnostic algorithms based on PCA-LDA. The results from this exploratory study demonstrate that SERS plasma analysis combined with PCA-LDA has tremendous potential for the non-invasive detection of gastric cancers.

Label-free detection of serum proteins using surface-enhanced Raman spectroscopy for colorectal cancer screening

Surface-enhanced Raman scattering (SERS) spectra of serum proteins purified from human serum samples were employed to detect colorectal cancer. Acetic acid as a new aggregating agent was introduced to increase the magnitude of the SERS enhancement. High-quality SERS spectra of serum proteins were acquired from 103 cancer patients and 103 healthy volunteers. Tentative assignments of SERS bands reflect that some specific biomolecular contents and protein secondary structures change with colorectal cancer progression. Principal component analysis combined with linear discriminant analysis was used to assess the capability of this approach for identifying colorectal cancer, yielding diagnostic accuracies of 100% (sensitivity: 100%; specificity: 100%) based on albumin SERS spectroscopy and 99.5% (sensitivity: 100%; specificity: 99%) based on globulin SERS spectroscopy, respectively. A partial least squares (PLS) approach was introduced to develop diagnostic models. An albumin PLS model successfully predicted the unidentified subjects with a diagnostic accuracy of 93.5% (sensitivity: 95.6%; specificity: 91.3%) and the globulin PLS model gave a diagnostic accuracy of 93.5% (sensitivity: 91.3%; specificity: 95.6%). These results suggest that serum protein SERS spectroscopy can be a sensitive and clinically powerful means for colorectal cancer detection.

Surface-enhanced Raman spectroscopy for differentiation between benign and malignant thyroid tissues

The aim of this study was to evaluate the potential of applying silver nano-particle based surface-enhanced Raman scattering (SERS) to discriminate different types of human thyroid tissues. SERS measurements were performed on three groups of tissue samples including thyroid cancers (n = 32), nodular goiters (n = 20) and normal thyroid tissues (n = 25). Tentative assignments of the measured tissue SERS spectra suggest interesting cancer specific biomolecular differences. The principal component analysis (PCA) and linear discriminate analysis (LDA) together with the leave-one-out, cross-validated technique yielded diagnostic sensitivities of 92%, 75% and 87.5%; and specificities of 82.6%, 89.4% and 84.4%, respectively, for differentiation among normal, nodular and malignant thyroid tissue samples. This work demonstrates that tissue SERS spectroscopy associated with multivariate analysis diagnostic algorithms has great potential for detection of thyroid cancer at the molecular level.

Saliva analysis combining membrane protein purification with surface-enhanced Raman spectroscopy for nasopharyngeal cancer detection

A method for saliva analysis combining membrane protein purification with silver nanoparticle-based surface-enhanced Raman spectroscopy (SERS) for non-invasive nasopharyngeal cancer detection was present in this paper. In this method, cellulose acetate membrane was used to obtain purified whole proteins from human saliva while removing other native saliva constituents and exogenous substances. The purified proteins were mixed with silver nanoparticle for SERS analysis. A diagnostic accuracy of 90.2% can be achieved by principal components analysis combined with linear discriminate analysis, for saliva samples obtained from patients with nasopharyngeal cancer (n = 62) and healthy volunteers (n = 30). This exploratory study demonstrated the potential for developing non-invasive, rapid saliva SERS analysis for nasopharyngeal cancer detection.

A noninvasive cancer detection strategy based on gold nanoparticle surface-enhanced raman spectroscopy of urinary modified nucleosides isolated by affinity chromatography.

The search for tumor biomarkers in the urine for cancer diagnosis is currently a hot topic in clinical oncology, with potential for cancer screening and diagnosis. Modified nucleosides excreted through the urine are considered to be a general tumor marker for various cancer types. Herein, we explore a new method that utilizes surface-enhanced Raman scattering (SERS) spectroscopy to obtain a complete biochemical profile of urinary modified nucleosides. In our method, modified nucleosides are first isolated from urine sample utilizing the excellent separation ability of affinity chromatography; then supplemented with gold (Au) nanoparticles as substrate for SERS spectroscopy analysis. The obtained SERS spectra present rich diagnostic and fingerprinting type signatures of urinary modified nucleosides. The utility of this new method in cancer detection was evaluated by analyzing urine samples from three groups of subjects: nasopharyngeal cancer patients (n=62), esophageal cancer patients (n=55), and healthy volunteers (n=52). Partial least squares and linear discriminant analysis (PLS-DA) were used to analyze and classify the SERS spectra of urinary modified nucleosides from nasopharyngeal cancer, esophageal cancer, and the normal group, achieving diagnostic sensitivities of 95.2%, 90.9% and 98.1% and specificities of 97.2%, 98.2% and 95.7%, respectively. These results demonstrated great potential of this novel method for non-invasive and label-free cancer detection and screening.

Confocal Raman spectroscopic analysis of the cytotoxic response to cisplatin in nasopharyngeal carcinoma cells

Apoptosis of nasopharyngeal carcinoma cells (C666 cell line) induced by an anticancer drug cisplatin was investigated by confocal Raman micro-spectroscopy using near-infrared laser (785 nm) excitation in this study. The Raman spectra of C666 cells treated with different concentrations of cisplatin (0.5, 1, 5 and 10 μg mL−1) for 24 h and different treatment times (6, 12, 18 and 24 h) with 5 μg mL−1 cisplatin were collected separately. Difference in the intensities of Raman peaks assigned to the DNA band (783 cm−1, 1338 cm−1, 1523 cm−1 and 1576 cm−1) between the cells treated with cisplatin and control cells becomes greater as the concentration of cisplatin increases, indicating that the cytotoxicity of cisplatin for NPC cells is likely related to its concentration. The major difference between the apoptotic C666 cells incubated with cisplatin and the non-treated cells is the reduction in intensities of vibration bands generated by cellular nucleic acids, proteins and lipids. Large intensity reduction in nucleic vibrations at 783, 1523 and 1576 cm−1 was observed upon apoptosis of the C666 cells. In particular, up to 14.1% and 49.6% reduction in the magnitude of the peaks at 783 cm−1 and 1523 cm−1 respectively in Raman spectra of the apoptotic cells was observed after 24 h of cisplatin treatment, which suggests the breakdown of phosphodiester bonds and DNA bases. Moreover, the intensity of peaks at 1002 and 1447 cm−1 respectively fell to 40.9% and 43.1% of the original value, which indicates that cisplatin could induce apoptosis of C666 cells and reduce the amount of nucleic acid and protein in the cells. These results demonstrate that Raman spectroscopy is a novel, nondestructive mean for studying the anticancer-treated carcinoma cells, which could also provide abundant information about the changes in biochemical properties of cells and serve as an effective method for real time measurement of apoptosis.

Investigation on the interactions of lymphoma cells with paclitaxel by Raman spectroscopy

The single-cell Raman spectra of human Burkitts lymphoma cells (CA46) including cells treated with different doses of paclitaxel and controls without paclitaxel can be detected by confocal micro-Raman spectroscopy. It shows that the Raman bands at 1094 cm–1 assigned to the symmetric stretching vibration mode of O–P–O in the DNA backbone, 1338 cm–1 and 1578 cm–1 due to adenine and guanine of DNA all decrease in intensity with increasing drug dose. On the contrary, the intensity of peaks at 1257 cm–1 due to characteristic vibration of a-helix of Amide III and 1658 cm–1 due to characteristic vibration of a-helix of Amide I both increases with increasing drug dose. Multivariate statistical methods, such as Principle Components Analysis (PCA) and Linear Discriminant Analysis (LDA) were employed to discriminate normal lymphoma cells (CA46) and cells treated with different doses of paclitaxel. It was found that the sensitivity and specificity of differentiating the treated and untreated cell groups increase with drug doses and approach 100% for the high drug dose, consistent with the perception that the cytotoxicity increases with drug dose. These results suggest that Raman spectroscopy combined with multivariate analysis could become a useful tool for assessing the cytotoxicity of drugs such as paclitaxel on human lymphoma cells.

Nondestructive discrimination between normal and hematological malignancy cell lines using near-infrared Raman spectroscopy and multivariate analysis

An accurate understanding of biomolecular changes in living cells associated with malignant transformation is of paramount importance in early cancer detection. The aim of this study was to apply near-infrared Raman spectroscopy (RS) for differentiating cancer from normal cells. High-quality Raman spectra in the range of 450–1800 cm−1 can be obtained from 31 normal and 64 hematological malignancy cells including 43 CA46 and 21 U266 cells. There were significant differences in Raman spectra between normal and cancer groups, which suggests special changes in the percentage of biomolecules including lipid, nucleic acids and proteins in different cell lines. A diagnostic accuracy of 100% can be achieved by principal components analysis (PCA) combined with linear discriminant analysis (LDA) for classification between cancer and normal cell lines. This exploratory study demonstrates the potential application of the RS technique combined with PCA–LDA as a clinical cell-based biosensor for the noninvasive cancer detection and screening at the molecular level.