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Raman spectroscopy with multivariate analysis: characterization and classification of biomarkers of pancreatic cancer

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Raman spectroscopy with multivariate analysis : characterization and classification of biomarkers of pancreatic cancer. / Mitchell, Alana; Martin-Hirsch, Pierre Leonard; Kauser, A.; Martin, Francis Luke; Chang, D.

In: HPB, Vol. 18, No. Supplement 2, 04.2016, p. e829-e830.

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Mitchell, Alana ; Martin-Hirsch, Pierre Leonard ; Kauser, A. ; Martin, Francis Luke ; Chang, D. / Raman spectroscopy with multivariate analysis : characterization and classification of biomarkers of pancreatic cancer. In: HPB. 2016 ; Vol. 18, No. Supplement 2. pp. e829-e830.

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@article{cd8fce58c0f64c82b96aee1283f350ef,
title = "Raman spectroscopy with multivariate analysis: characterization and classification of biomarkers of pancreatic cancer",
abstract = "Aims: Pancreatic cancer remains an insidious condition for which early diagnosis would improve prognosis. Biospectroscopy has been proposed as a reagent-free, non-destructive approach towards screening in biofluids1. Raman spectroscopy generates bio-fingerprint spectra. Within a computational framework, such spectra may objectively classify presence or absence of underlying disease.Methods: A total of n = 13 plasma samples (10 from patients with pancreatic cancer and 3 controls) and n = 15 urine samples (12 pancreatic cancer and 3 controls) were obtained following ethical approval. Ten Raman spectra were acquired using 10% laser power from 200 μl of each sample deposited and dried on slides. Pre-processed spectra were analysed by principal component analysis using MATLAB software2. Vector loadings analysis determined principle variables (or peaks) responsible for discriminating cancer vs. controls.Results: Raman spectra were readily derived. Identifying differences between spectra from different categories is challenging3, so computational algorithms are required. Each spectrum is reduced to a single point in n-dimensional hyperspace, allowing comparison between different spectra. Following vector loading analysis, several wavenumbers appear important, namely, variables in the 1720–1755 cm−1 region and the 900–1000 cm−1 spectral regions.Conclusions: The variables between 1755–1720 cm−1 are associated with C = O stretching vibrations of aldehydes and lipids. Wavenumbers between 900–1000 cm−1 represent spectral regions of DNA/RNA vibrations. Spectral fingerprints combined with computational analysis offers exciting opportunities in biomarker development, screening and diagnosis. For conditions such as pancreatic cancer, the possibility of inexpensive, point-of-care testing has enormous possibilities for monitoring at-risk individuals.",
author = "Alana Mitchell and Martin-Hirsch, {Pierre Leonard} and A. Kauser and Martin, {Francis Luke} and D. Chang",
year = "2016",
month = apr
doi = "10.1016/j.hpb.2016.01.413",
language = "English",
volume = "18",
pages = "e829--e830",
journal = "HPB",
issn = "1365-182X",
publisher = "Elsevier",
number = "Supplement 2",

}

RIS

TY - JOUR

T1 - Raman spectroscopy with multivariate analysis

T2 - characterization and classification of biomarkers of pancreatic cancer

AU - Mitchell, Alana

AU - Martin-Hirsch, Pierre Leonard

AU - Kauser, A.

AU - Martin, Francis Luke

AU - Chang, D.

PY - 2016/4

Y1 - 2016/4

N2 - Aims: Pancreatic cancer remains an insidious condition for which early diagnosis would improve prognosis. Biospectroscopy has been proposed as a reagent-free, non-destructive approach towards screening in biofluids1. Raman spectroscopy generates bio-fingerprint spectra. Within a computational framework, such spectra may objectively classify presence or absence of underlying disease.Methods: A total of n = 13 plasma samples (10 from patients with pancreatic cancer and 3 controls) and n = 15 urine samples (12 pancreatic cancer and 3 controls) were obtained following ethical approval. Ten Raman spectra were acquired using 10% laser power from 200 μl of each sample deposited and dried on slides. Pre-processed spectra were analysed by principal component analysis using MATLAB software2. Vector loadings analysis determined principle variables (or peaks) responsible for discriminating cancer vs. controls.Results: Raman spectra were readily derived. Identifying differences between spectra from different categories is challenging3, so computational algorithms are required. Each spectrum is reduced to a single point in n-dimensional hyperspace, allowing comparison between different spectra. Following vector loading analysis, several wavenumbers appear important, namely, variables in the 1720–1755 cm−1 region and the 900–1000 cm−1 spectral regions.Conclusions: The variables between 1755–1720 cm−1 are associated with C = O stretching vibrations of aldehydes and lipids. Wavenumbers between 900–1000 cm−1 represent spectral regions of DNA/RNA vibrations. Spectral fingerprints combined with computational analysis offers exciting opportunities in biomarker development, screening and diagnosis. For conditions such as pancreatic cancer, the possibility of inexpensive, point-of-care testing has enormous possibilities for monitoring at-risk individuals.

AB - Aims: Pancreatic cancer remains an insidious condition for which early diagnosis would improve prognosis. Biospectroscopy has been proposed as a reagent-free, non-destructive approach towards screening in biofluids1. Raman spectroscopy generates bio-fingerprint spectra. Within a computational framework, such spectra may objectively classify presence or absence of underlying disease.Methods: A total of n = 13 plasma samples (10 from patients with pancreatic cancer and 3 controls) and n = 15 urine samples (12 pancreatic cancer and 3 controls) were obtained following ethical approval. Ten Raman spectra were acquired using 10% laser power from 200 μl of each sample deposited and dried on slides. Pre-processed spectra were analysed by principal component analysis using MATLAB software2. Vector loadings analysis determined principle variables (or peaks) responsible for discriminating cancer vs. controls.Results: Raman spectra were readily derived. Identifying differences between spectra from different categories is challenging3, so computational algorithms are required. Each spectrum is reduced to a single point in n-dimensional hyperspace, allowing comparison between different spectra. Following vector loading analysis, several wavenumbers appear important, namely, variables in the 1720–1755 cm−1 region and the 900–1000 cm−1 spectral regions.Conclusions: The variables between 1755–1720 cm−1 are associated with C = O stretching vibrations of aldehydes and lipids. Wavenumbers between 900–1000 cm−1 represent spectral regions of DNA/RNA vibrations. Spectral fingerprints combined with computational analysis offers exciting opportunities in biomarker development, screening and diagnosis. For conditions such as pancreatic cancer, the possibility of inexpensive, point-of-care testing has enormous possibilities for monitoring at-risk individuals.

U2 - 10.1016/j.hpb.2016.01.413

DO - 10.1016/j.hpb.2016.01.413

M3 - Meeting abstract

VL - 18

SP - e829-e830

JO - HPB

JF - HPB

SN - 1365-182X

IS - Supplement 2

ER -