Raman spectroscopy and surface-enhanced Raman spectroscopy (SERS) spectra of salivary glands carcinoma, tumor and healthy tissues and their homogenates analyzed by chemometry: Towards development of the novel tool for clinical diagnosis

Czaplicka, Marta; Kowalska, Aneta; Nowicka, Ariadna B.; Kurzydłowski, Dominik; Gronkiewicz, Zuzanna; Machulak, A.; Kukwa, Wojciech; Kamińska, Agnieszka

doi:10.1016/j.aca.2021.338784

Cited by 30 publications

(16 citation statements)

References 38 publications

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“…A disadvantage of FTIR is the time needed for sample preparation. Raman spectroscopy, unlike FTIR, is faster in preparing samples [ 21 , 26 , 27 , 28 ].…”

Section: Discussionmentioning

confidence: 99%

A Tissue Section-Based Near-Infrared Spectroscopical Analysis of Salivary Gland Tumors

Coopman

Bruyne

Speeckaert

et al. 2021

Cancers

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SGTs vary in histological behavior. Mucins, a major component in salivary glands, consist of a glycosylated and sialylated protein core. Rapid evolutions in glycobiology have demonstrated the important role of glycoproteins in cancer development. NIR spectroscopy is a method for the biochemical analysis of substrates. NIR spectra can be analyzed using specific chemometrics. Our aim was to explore the diagnostic possibilities of NIR spectroscopy in SGTs. 238 Hematoxylin and Eosine stained (H&E) SGT tissue sections were examined using NIR spectroscopy. 45 deparaffinized tissue sections were treated with neuraminidase to identify wavelengths in the NIR spectrum related to sialylation. NIR spectra were analyzed with chemometrics. NIR spectra could distinguish malignant SGTs from controls and benign SGTs. Prediction models based on the entire spectral range resulted in a 73.1% accurate classification of malignant SGTs and controls, while, based on neuraminidase experimental spectral peak differences (1436 nm; 1713 nm; 1783 nm; 1924 nm; 2032 nm; 2064 nm; 2178 nm; 2216 nm), an improved overall correct classification rate of 91.9% was obtained between healthy subjects and malignant tumors. H&E tissue section-based NIR spectroscopy can identify malignant SGTs from controls, promising an alternative method in the diagnosis of SGTs.

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“…A disadvantage of FTIR is the time needed for sample preparation. Raman spectroscopy, unlike FTIR, is faster in preparing samples [ 21 , 26 , 27 , 28 ].…”

Section: Discussionmentioning

confidence: 99%

A Tissue Section-Based Near-Infrared Spectroscopical Analysis of Salivary Gland Tumors

Coopman

Bruyne

Speeckaert

et al. 2021

Cancers

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“… 209 By applying PCA, one may discover hidden patterns in the original data with minimal intervention (see Figure 6 a). 210 − 213 However, as long as labeled data are available, which is often the case in normal and altered conditions (e.g., classification of healthy vs cancer cells), a supervised algorithm would be more reliable in SERS analysis. Indeed, the separation of classes is not a trivial task because even spectra from the same class may vary significantly, for example, one single metabolite might exhibit different spectra depending on its molecular orientation on the SERS substrate.…”

Section: Hitting the Target With The Aid Of Artificial Intelligencementioning

confidence: 99%

“…Multiple strategies, from “simple” exploratory analyses through machine learning methods, have been exploited for pattern-recognition and classification of SERS spectra. , Unsupervised methods, such as principal component analysis (PCA), are routinely used to visualize variations in the data by compressing the dimension of the SERS spectra, with minimal loss or reduction of uncorrelated variables . By applying PCA, one may discover hidden patterns in the original data with minimal intervention (see Figure a). − However, as long as labeled data are available, which is often the case in normal and altered conditions (e.g., classification of healthy vs cancer cells), a supervised algorithm would be more reliable in SERS analysis. Indeed, the separation of classes is not a trivial task because even spectra from the same class may vary significantly, for example, one single metabolite might exhibit different spectra depending on its molecular orientation on the SERS substrate.…”

Section: Hitting the Target With The Aid Of Artificial Intelligencementioning

confidence: 99%

Prospects of Surface-Enhanced Raman Spectroscopy for Biomarker Monitoring toward Precision Medicine

et al. 2022

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Future precision medicine will be undoubtedly sustained by the detection of validated biomarkers that enable a precise classification of patients based on their predicted disease risk, prognosis, and response to a specific treatment. Up to now, genomics, transcriptomics, and immunohistochemistry have been the main clinically amenable tools at hand for identifying key diagnostic, prognostic, and predictive biomarkers. However, other molecular strategies, including metabolomics, are still in their infancy and require the development of new biomarker detection technologies, toward routine implementation into clinical diagnosis. In this context, surface-enhanced Raman scattering (SERS) spectroscopy has been recognized as a promising technology for clinical monitoring thanks to its high sensitivity and label-free operation, which should help accelerate the discovery of biomarkers and their corresponding screening in a simpler, faster, and less-expensive manner. Many studies have demonstrated the excellent performance of SERS in biomedical applications. However, such studies have also revealed several variables that should be considered for accurate SERS monitoring, in particular, when the signal is collected from biological sources (tissues, cells or biofluids). This Perspective is aimed at piecing together the puzzle of SERS in biomarker monitoring, with a view on future challenges and implications. We address the most relevant requirements of plasmonic substrates for biomedical applications, as well as the implementation of tools from artificial intelligence or biotechnology to guide the development of highly versatile sensors.

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“…Therefore, considerable efforts are taken to find the best method for gathering the spectral data of the tumor samples. In this sense, we invented the homogenates preparation, [33,34] which allows to gain satisfactory SERS signal. Tissue fragments were prepared according to an already published procedure.…”

Section: Tissue Homogenates Preparationmentioning

confidence: 99%

Association between grade brain tumors and the interleukin‐10 receptor subunit alpha based on surface‐enhanced Raman spectroscopy and multivariate analysis

Kowalska

Czaplicka

Nowicka

et al. 2021

J Raman Spectroscopy

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We present herein a label-free method based on surface-enhanced Raman spectroscopy (SERS) that was performed using a silicon-based SERS platform. The differentiation between primary and secondary brain tumors SERS data was completed partial least squares (PLS) method with a very high 85% of accuracy (in only first and second factors), whereas calculated using principal component analysis (PCA) method gives 74% (in two consecutive components). Additionally, due to the fact that the interleukin-10 (IL-10) cytokine receptor may act in cancer as both an immunosuppressive and an immunostimulant factor, the correlation between the tumor grading and the cytokine receptor are presented. Obtained data indicate that the function of IL-10 cytokine receptor subunit alpha most probably depends or is closely related to the tumor stage.

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Raman spectroscopy and surface-enhanced Raman spectroscopy (SERS) spectra of salivary glands carcinoma, tumor and healthy tissues and their homogenates analyzed by chemometry: Towards development of the novel tool for clinical diagnosis

Cited by 30 publications

References 38 publications

A Tissue Section-Based Near-Infrared Spectroscopical Analysis of Salivary Gland Tumors

A Tissue Section-Based Near-Infrared Spectroscopical Analysis of Salivary Gland Tumors

Prospects of Surface-Enhanced Raman Spectroscopy for Biomarker Monitoring toward Precision Medicine

Association between grade brain tumors and the interleukin‐10 receptor subunit alpha based on surface‐enhanced Raman spectroscopy and multivariate analysis

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