Salivary Raman Spectroscopy: Standardization of Sampling Protocols and Stratification of Healthy and Oral Cancer Subjects

Minimally invasive cancer detection using bio-fluids has been actively pursued due to practical limitations, though there are better suited noninvasive and online in vivo methods. Saliva is one such clinically informative bio-fluid that offers the advantages of easy and multiple sample collection. Despite its potential in cancer diagnostics, saliva analysis is challenging due to its heterogeneous composition. Recently, there has been an upsurge in saliva exploration using optical techniques. Forms of saliva such as precipitate and supernatant have been monitored, but this sampling method needs to be standardized due to the obvious loss of analytes in processing. In that context, present work details the comparison of four different saliva sampling methodologies, i.e., air-dried, lyophilized, pellet, and supernatant using Raman spectroscopy collected from 10 healthy samples. Composition-driven spectral features of all forms were compared and classified using principal component analysis and linear discriminant analysis. Analysis was carried out on all four groups in the first step. In the second step, groups of pellet and supernatant , and air-dried and lyophilized were analyzed. Findings suggest that pellet and supernatant exhibit discrete spectroscopic features and demonstrate high classification efficiency, which is indicative of their distinctive biochemical composition. On the other hand, air-dried and lyophilized forms showed overlapping spectral features and low classification, suggesting these forms retain majority spectroscopic features of whole saliva and are less prone to sampling losses. Thus, this study indicates air-dried and lyophilized forms may be more appropriate for saliva sampling using Raman spectroscopy providing the comprehensive information required for cancer diagnosis. Furthermore, the method was also tested for the classification of oral cancer and healthy subjects ( n = 27) which yielded 90% stratification. The findings of the study indicate the utility of minimally invasive salivary Raman-based diagnostics in oral cancers.

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Current and Future Advancements of Raman Spectroscopy Techniques in Cancer Nanomedicine

International Journal of Molecular Sciences ◽

10.3390/ijms222313141 ◽

2021 ◽

Vol 22 (23) ◽

pp. 13141

Author(s):

Elisabetta Canetta

Keyword(s):

Raman Spectroscopy ◽

Imaging Techniques ◽

Rapid Development ◽

Surface Enhanced Raman Spectroscopy ◽

Cancer Diagnostics ◽

Raman Signal ◽

Therapeutic Effects ◽

Cancer Nanomedicine

Raman scattering is one of the most used spectroscopy and imaging techniques in cancer nanomedicine due to its high spatial resolution, high chemical specificity, and multiplexity modalities. The flexibility of Raman techniques has led, in the past few years, to the rapid development of Raman spectroscopy and imaging for nanodiagnostics, nanotherapy, and nanotheranostics. This review focuses on the applications of spontaneous Raman spectroscopy and bioimaging to cancer nanotheranostics and their coupling to a variety of diagnostic/therapy methods to create nanoparticle-free theranostic systems for cancer diagnostics and therapy. Recent implementations of confocal Raman spectroscopy that led to the development of platforms for monitoring the therapeutic effects of anticancer drugs in vitro and in vivo are also reviewed. Another Raman technique that is largely employed in cancer nanomedicine, due to its ability to enhance the Raman signal, is surface-enhanced Raman spectroscopy (SERS). This review also explores the applications of the different types of SERS, such as SERRS and SORS, to cancer diagnosis through SERS nanoprobes and the detection of small-size biomarkers, such as exosomes. SERS cancer immunotherapy and immuno-SERS (iSERS) microscopy are reviewed.

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Raman Spectroscopy Analysis for Optical Diagnosis of Oral Cancer Detection

Journal of Clinical Medicine ◽

10.3390/jcm8091313 ◽

2019 ◽

Vol 8 (9) ◽

pp. 1313 ◽

Cited By ~ 9

Author(s):

Ming-Jer Jeng ◽

Mukta Sharma ◽

Lokesh Sharma ◽

Ting-Yu Chao ◽

Shiang-Fu Huang ◽

...

Keyword(s):

Raman Spectroscopy ◽

Discriminant Analysis ◽

Oral Cancer ◽

Cross Validation ◽

Principal Component ◽

Beta Carotene ◽

Invasive Technique ◽

Protein Amino Acid ◽

Linear Discriminant ◽

Model Studies

Raman spectroscopy (RS) is widely used as a non-invasive technique in screening for the diagnosis of oral cancer. The potential of this optical technique for several biomedical applications has been proved. This work studies the efficacy of RS in detecting oral cancer using sub-site-wise differentiation. A total of 80 samples (44 tumor and 36 normal) were cryopreserved from three different sub-sites: The tongue, the buccal mucosa, and the gingiva of the oral mucosa during surgery. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were used with principal component analysis (PCA) to classify the samples and the classifications were validated by leave-one-out-cross-validation (LOOCV) and k-fold cross-validation methods. The normal and tumor tissues were differentiated under the PCA-LDA model with an accuracy of 81.25% (sensitivity: 77.27%, specificity: 86.11%). The PCA-QDA classifier model differentiated these tissues with an accuracy of 87.5% (sensitivity: 90.90%, specificity: 83.33%). The PCA-QDA classifier model outperformed the PCA-LDA-based classifier. The model studies revealed that protein, amino acid, and beta-carotene variations are the main biomolecular difference markers for detecting oral cancer.

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

Journal of Spectroscopy ◽

10.1155/2016/1603609 ◽

2016 ◽

Vol 2016 ◽

pp. 1-6 ◽

Cited By ~ 14

Author(s):

Wenjing Liu ◽

Zhaotian Sun ◽

Jinyu Chen ◽

Chuanbo Jing

Keyword(s):

Colorectal Cancer ◽

Raman Spectroscopy ◽

Discriminant Analysis ◽

Diagnostic Accuracy ◽

Raman Spectra ◽

Principal Component ◽

Cancer Diagnostics ◽

Analysis Model ◽

Tissue Samples ◽

Linear Discriminant

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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Novel Quantitative Analysis Using Optical Imaging (VELscope) and Spectroscopy (Raman) Techniques for Oral Cancer Detection

Cancers ◽

10.3390/cancers12113364 ◽

2020 ◽

Vol 12 (11) ◽

pp. 3364

Author(s):

Ming-Jer Jeng ◽

Mukta Sharma ◽

Lokesh Sharma ◽

Shiang-Fu Huang ◽

Liann-Be Chang ◽

...

Keyword(s):

Raman Spectroscopy ◽

Quantitative Analysis ◽

Discriminant Analysis ◽

Oral Cancer ◽

Raman Spectra ◽

Cross Validation ◽

Error Rates ◽

Control Group ◽

Optical Techniques ◽

Linear Discriminant

In this study, we developed a novel quantitative analysis method to enhance the detection capability for oral cancer screening. We combined two different optical techniques, a light-based detection technique (visually enhanced lesion scope) and a vibrational spectroscopic technique (Raman spectroscopy). Materials and methods: Thirty-five oral cancer patients who went through surgery were enrolled. Thirty-five cancer lesions and thirty-five control samples with normal oral mucosa (adjacent to the cancer lesion) were analyzed. Thirty-five autofluorescence images and 70 Raman spectra were taken from 35 cancer and 35 control group cryopreserved samples. The normalized intensity and heterogeneity of the 70 regions of interest (ROIs) were calculated along with 70 averaged Raman spectra. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were used with principal component analysis (PCA) to differentiate the cancer and control groups (normal). The classifications rates were validated using two different validation methods, leave-one-out cross-validation (LOOCV) and k-fold cross-validation. Results: The cryopreserved normal and tumor tissues were differentiated using the PCA–LDA and PCA–QDA models. The PCA–LDA of Raman spectroscopy (RS) had 82.9% accuracy, 80% sensitivity, and 85.7% specificity, while ROIs on the autofluorescence images were differentiated with 90% accuracy, 100% sensitivity, and 80% specificity. The combination of two optical techniques differentiated cancer and normal group with 97.14% accuracy, 100% sensitivity, and 94.3% specificity. Conclusion: In this study, we combined the data of two different optical techniques. Furthermore, PCA–LDA and PCA–QDA quantitative analysis models were used to differentiate tumor and normal groups, creating a complementary pathway for efficient tumor diagnosis. The error rates of RS and VELcope analysis were 17.10% and 10%, respectively, which was reduced to 3% when the two optical techniques were combined.

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RAMAN SPECTROSCOPIC STUDY ON PREDICTION OF TREATMENT RESPONSE IN CERVICAL CANCERS

Journal of Innovative Optical Health Sciences ◽

10.1142/s1793545813500144 ◽

2013 ◽

Vol 06 (02) ◽

pp. 1350014 ◽

Cited By ~ 8

Author(s):

S. RUBINA ◽

M. S. VIDYASAGAR ◽

C. MURALI KRISHNA

Keyword(s):

Raman Spectroscopy ◽

Tumor Response ◽

Locally Advanced ◽

Principal Component ◽

Diagnostic Tools ◽

Spectroscopic Techniques ◽

Predictive Tool ◽

Linear Discriminant ◽

Cervical Cancers

Concurrent chemoradiotherapy (CCRT) is the choice of treatment for locally advanced cervical cancers; however, tumors exhibit diverse response to treatment. Early prediction of tumor response leads to individualizing treatment regimen. Response evaluation criteria in solid tumors (RECIST), the current modality of tumor response assessment, is often subjective and carried out at the first visit after treatment, which is about four months. Hence, there is a need for better predictive tool for radioresponse. Optical spectroscopic techniques, sensitive to molecular alteration, are being pursued as potential diagnostic tools. Present pilot study aims to explore the fiber-optic-based Raman spectroscopy approach in prediction of tumor response to CCRT, before taking up extensive in vivo studies. Ex vivo Raman spectra were acquired from biopsies collected from 11 normal (148 spectra), 16 tumor (201 spectra) and 13 complete response (151 CR spectra), one partial response (8 PR spectra) and one nonresponder (8 NR spectra) subjects. Data was analyzed using principal component linear discriminant analysis (PC-LDA) followed by leave-one-out cross-validation (LOO-CV). Findings suggest that normal tissues can be efficiently classified from both pre- and post-treated tumor biopsies, while there is an overlap between pre- and post-CCRT tumor tissues. Spectra of CR, PR and NR tissues were subjected to principal component analysis (PCA) and a tendency of classification was observed, corroborating previous studies. Thus, this study further supports the feasibility of Raman spectroscopy in prediction of tumor radioresponse and prospective noninvasive in vivo applications.

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Surface Enhanced Raman Spectroscopy Based Biosensor with a Microneedle Array for Minimally Invasive In Vivo Glucose Measurements

ACS Sensors ◽

10.1021/acssensors.0c00444 ◽

2020 ◽

Vol 5 (6) ◽

pp. 1777-1785 ◽

Cited By ~ 5

Author(s):

Jian Ju ◽

Chao-Mao Hsieh ◽

Yao Tian ◽

Jian Kang ◽

Ruining Chia ◽

...

Keyword(s):

Raman Spectroscopy ◽

Minimally Invasive ◽

Surface Enhanced Raman Spectroscopy ◽

Microneedle Array ◽

Surface Enhanced ◽

Surface Enhanced Raman

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Combinatorial Characterization of Saliva for Oral Precancer Diagnostics

Journal of Global Oncology ◽

10.1200/jgo.18.85500 ◽

2018 ◽

Vol 4 (Supplement 2) ◽

pp. 212s-212s

Author(s):

N. Sharma ◽

S. Bag ◽

K. Biswas ◽

M. Pal ◽

R.R. Paul ◽

...

Keyword(s):

Oral Cancer ◽

Oral Submucous Fibrosis ◽

Cancer Diagnostics ◽

Matrix Analysis ◽

Sample Collection ◽

Combinatorial Characterization ◽

Oral Potentially Malignant Disorders ◽

Custom Made ◽

Component Matrix

Background: Saliva based diagnostic can play an important role in the translational research related to cancer diagnostics and treatment. It is easily available, noninvasive, low storage cost, has less contamination chances with simple collection procedure. Cancers related to tobacco use, including oral cancer account for about 30% of all cancers in males and females. Five years' survival rate remains the same even after decades of advancement of detection, prevention, and treatment of OSCC (oral squamous cell carcinoma) mainly due to late diagnosis of oral potentially malignant disorders (OPMDs). Aim: Combinatorial characterization of saliva, endorsing multidimensional spectroscopic signatures using suitably designed biochamber. Methods: Eighteen saliva samples (6 normal, 6 OSF [oral submucous fibrosis, a type of OPMD] and 6 confirmed OSCC) were collected from GNIDSR (Guru Nanak Institute of Dental Science and Research) Kolkata. Ethical approval was obtained for the study and all the participants were explained the objectives of the study and a written informed consent was obtained from them. Participant's demographic detail and clinical characteristics were also recorded. The participants were asked not to consume food 1 hour before sample collection and were suggested to rinse their mouth 30 minutes prior to saliva expectoration to minimize the contamination of food in saliva. Empty, sterile, graded tubes were used for this purpose. The subjects were asked not to clear nose or throat during the process of saliva expectoration to avoid forced phlegm from other part of the respiratory tract. The saliva samples were then immediately transferred to −20 degrees and later in −80 degrees for long storage. The electrical impedance (EI) of saliva was measured in custom made biochambers with copper electrodes. The EI was measured for the frequency sweep from 20 Hz to 2 MHz using an impedance analyzer. Apart from EI measurement, the corresponding samples were subjected to FTIR (Fourier-transform IR spectroscopy) analysis. SPSS and OMNIC software were used for the data analysis of EI and FTIR respectively. Results: [Table: see text][Table: see text][Table: see text][ Table A , B & C represents descriptive statistics, correlation matrix and component matrix respectively. The multivariate analysis of the FTIR data indicates the significant differences ( P < 0.005) among the different study groups such as normal, OSF and OSCC. The eigen values (normal 0.917, OSF 0.962, OSCC 0.975) from component matrix analysis also indicate the same. Conclusion: The spectroscopic characterization (EI and FTIR) of saliva was effective in evaluating normal and OPMD condition. This noninvasive paradigm can serve as a complimentary technique to the existing gold standard methods for the early detection of oral cancer.

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Developing an Algorithm for Discriminating Oral Cancerous and Normal Tissues Using Raman Spectroscopy

Journal of Personalized Medicine ◽

10.3390/jpm11111165 ◽

2021 ◽

Vol 11 (11) ◽

pp. 1165

Author(s):

Mukta Sharma ◽

Ming-Jer Jeng ◽

Chi-Kuang Young ◽

Shiang-Fu Huang ◽

Liann-Be Chang

Keyword(s):

Raman Spectroscopy ◽

Surgical Resection ◽

Ex Vivo ◽

Spectroscopic Method ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Validation Dataset ◽

Linear Discriminant

The aim of this study was to investigate the clinical potential of Raman spectroscopy (RS) in detecting oral squamous cell carcinoma (OSCC) in tumor and healthy tissues in surgical resection specimens during surgery. Raman experiments were performed on cryopreserved specimens from patients with OSCC. Univariate and multivariate analysis was performed based on the fingerprint region (700–1800 cm−1) of the Raman spectra. One hundred thirty-one ex-vivo Raman experiments were performed on 131 surgical resection specimens obtained from 67 patients. The principal component analysis (PCA) and partial least square (PLS) methods with linear discriminant analysis (LDA) were applied on an independent validation dataset. Both models were able to differentiate between the tissue types, but PLS–LDA showed 100% accuracy, sensitivity, and specificity. In this study, Raman measurements of fresh resection tissue specimens demonstrated that OSCC had significantly higher nucleic acid, protein, and several amino acid contents than adjacent healthy tissues. The specific spectral information obtained in this study can be used to develop an in vivo Raman spectroscopic method for the tumor-free resection boundary during surgery.

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