Raman spectroscopy at different excitation wavelengths (1064, 785 and 532 nm) as a tool for diagnosis of colon cancer

Synytsya, Alla; Judexová, Miluše; Hoskovec, David; Miškovičová, Michaela; Petruželka, Luboš

doi:10.1002/jrs.4581

Cited by 62 publications

(55 citation statements)

References 60 publications

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“…Figure 7a, brown bars are indicated on the peak, which is the main factor above the graph drawn as the average of total data. The main peaks for the control and IP/BPS samples were observed at 641, 683, 723, 873, 1002, 1030, and 1355 cm −1 , which corresponded to C-C twisting mode of tyrosine [37,38], ring breathing of nucleic acids for G [38,39] and A [39,40], C-C stretch of hydroxyproline [37,41], symmetric ring breathing mode [37][38][39][40][41] and C-H in-plane bending mode of phenylalanine [37,41], and CH 3 CH 2 wagging mode of collagen [37,41], respectively.…”

Section: Bio-applicationmentioning

confidence: 98%

Surface-Enhanced Raman Spectroscopy (SERS) Based on ZnO Nanorods for Biological Applications

Lee¹,

Kim²

2019

Zinc Oxide Based Nano Materials and Devices

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Detection of nanometer-sized biomarkers is a research topic that attracts much attention as an application for early diagnosis of diseases. Biopsy monitoring by analyzing cell secretion in a non-destructive way has many advantages in the field of biomedicine. We introduce the Raman signal enhancement method on a biosensing chip based on surface-enhanced Raman diagnosis. This approach has the advantage because the ZnO nanorods are grown to form nanoscale porosity and are coated with gold to enable size selective biomarker detection. After sputtering gold on the grown ZnO nanostructures, the unique feature of clustering the nanorod's heads first appeared. The grain formation on the head was the main factor for the localized surface plasmon resonance (LSPR) enhancement, and this fact could be verified by finite element analysis. It has been demonstrated in breast cancer cell line that the cell viability is also high in such gold-clad ZnO nanostructure-based surface-enhanced substrates. For bioapplication, interstitial cystitis/bladder pain syndrome (IC/BPS) animal model was prepared by injecting HCl into the bladder of a rat, and urine was collected a week later to conduct Raman spectroscopy experiments.

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Section: Bio-applicationmentioning

confidence: 98%

Surface-Enhanced Raman Spectroscopy (SERS) Based on ZnO Nanorods for Biological Applications

Lee¹,

Kim²

2019

Zinc Oxide Based Nano Materials and Devices

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“…24 785 nm laser excitation was purposely chosen for Raman spectroscopy, since it offers an excellent balance between Raman efficiency, sample fluorescence, and heat absorption. 25 Although the maximum LSPR peak is far from our excitation wavelength, components in the blood plasma (such as proteins, analytes, and salts) can contribute to the analyte-colloid and colloid-colloid interactions, which finally cause nanoparticles to aggregate, resulting in a red-shift of the LSPR. 26 This phenomenon can be easily confirmed by plotting the absorption spectra with different volume ratios of blood plasma and concentrated AgNPs (see Figure S3).…”

Section: Optimization Of Sers Signalsmentioning

confidence: 99%

<p>Filter-Membrane-Based Ultrafiltration Coupled with Surface-Enhanced Raman Spectroscopy for Potential Differentiation of Benign and Malignant Thyroid Tumors from Blood Plasma</p>

Liang¹,

Miao²,

Xiao³

et al. 2020

IJN

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The objective of this study is to evaluate the performance and feasibility of surface-enhanced Raman spectroscopy coupled with a filter membrane and advanced multivariate data analysis on identifying and differentiating benign and malignant thyroid tumors from blood plasma. Patients and Methods: We proposed a membrane filter SERS technology for the differentiation between benign thyroid tumor and thyroid cancer. That is to say, by using filter membranes with optimal pore size, the blood plasma samples from thyroid tumor patients were pretreated with the macromolecular proteins being filtered out prior to SERS measurement. The SERS spectra of blood plasma ultrafiltrate obtained using filter membranes from 102 patients with thyroid tumors (70 thyroid cancers and 32 benign thyroid tumors) were then analyzed and compared. Two multivariate statistical analyses, principal component analysis-linear discriminate analysis (PCA-LDA) and Lasso-partial least squaresdiscriminant analysis (Lasso-PLS-DA), were performed on the SERS spectral data after background subtraction and normalization, as well as the first derivative processing, to analyze and compare the differential diagnosis of benign thyroid tumors and thyroid cancer. Results: SERS measurements were performed in blood plasma acquired from a total of 102 thyroid tumor patients (benign thyroid tumor N=32; thyroid cancer N=70). By using filter membranes, the macromolecular proteins in blood plasma were effectively filtered out to yield high-quality SERS spectra. 84.3% discrimination accuracy between benign and malignant thyroid tumor was achieved using PCA-LDA method, while Lasso-PLS-DA yields a discrimination accuracy of 90.2%. Conclusion: Our results demonstrate that SERS spectroscopy, coupled with ultrafiltration and multivariate analysis has the potential of providing a non-invasive, rapid, and objective detection and differentiation of benign and malignant thyroid tumors.

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“…Principal component analysis (PCA), a frequently used unsupervised approach, reduces the number of variables and assesses the data as a first step. Following PCA, a supervised approach such as linear discriminant analysis (LDA), which takes advantage of PCA and the histopathological results, can classify tissues or cells [12][13][14]. A study on mice with colon cancer showed that the PCA-LDA model can correctly discriminate tumors from healthy tissues with an accuracy of 86.8% [15].…”

Section: Introductionmentioning

confidence: 99%

Raman Spectroscopy in Colorectal Cancer Diagnostics: Comparison of PCA-LDA and PLS-DA Models

Liu

Sun²,

Chen

et al. 2016

Journal of Spectroscopy

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Raman spectra of human colorectal tissue samples were employed to diagnose colorectal cancer. High-quality Raman spectra were acquired from normal and cancerous colorectal tissues from 81 patients. Subtle Raman variations, such as for peaks at 1134 cm−1 (protein, C-C/C-N stretching) and 1297 cm−1 (lipid, C-H2 twisting), were observed between normal and cancerous colorectal tissues. The average peak intensity at 1134 and 1297 cm−1 was increased from approximately 235 and 72 in the normal group, respectively, to 315 and 273 in the cancer group. The variations of Raman spectra reflected the changes of cell molecules during canceration. The multivariate statistical methods of principal component analysis-linear discriminant analysis (PCA-LDA) and partial least-squares-discriminant analysis (PLS-DA), together with leave-one-patient-out cross-validation, were employed to build the discrimination model. PCA-LDA was used to evaluate the capability of this approach for classifying colorectal cancer, resulting in a diagnostic accuracy of 79.2%. Further PLS-DA modeling yielded a diagnostic accuracy of 84.3% for colorectal cancer detection. Thus, the PLS-DA model is preferable between the two to discriminate cancerous from normal tissues. Our results demonstrate that Raman spectroscopy can be used with an optimized multivariate data analysis model as a sensitive diagnostic alternative to identify pathological changes in the colon at the molecular level.

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Raman spectroscopy at different excitation wavelengths (1064, 785 and 532 nm) as a tool for diagnosis of colon cancer

Cited by 62 publications

References 60 publications

Surface-Enhanced Raman Spectroscopy (SERS) Based on ZnO Nanorods for Biological Applications

Surface-Enhanced Raman Spectroscopy (SERS) Based on ZnO Nanorods for Biological Applications

<p>Filter-Membrane-Based Ultrafiltration Coupled with Surface-Enhanced Raman Spectroscopy for Potential Differentiation of Benign and Malignant Thyroid Tumors from Blood Plasma</p>

Raman Spectroscopy in Colorectal Cancer Diagnostics: Comparison of PCA-LDA and PLS-DA Models

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