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Comprehensive Serum Glycopeptide Spectra Analysis (CSGSA): A Potential New Tool for Early Detection of Ovarian Cancer
Objectives: To conduct a comprehensive glycopeptide spectra analysis of serum between cancer and non-cancer patients to identify early biomarkers of epithelial ovarian cancer (EOC). Methods: Approximately 30,000 glycopeptide peaks were detected from the digested serum glycoproteins of 39 EOC patient...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6563019/ https://www.ncbi.nlm.nih.gov/pubmed/31035594 http://dx.doi.org/10.3390/cancers11050591 |
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author | Hayashi, Masaru Matsuo, Koji Tanabe, Kazuhiro Ikeda, Masae Miyazawa, Mariko Yasaka, Miwa Machida, Hiroko Shida, Masako Imanishi, Tadashi Grubbs, Brendan H. Hirasawa, Takeshi Mikami, Mikio |
author_facet | Hayashi, Masaru Matsuo, Koji Tanabe, Kazuhiro Ikeda, Masae Miyazawa, Mariko Yasaka, Miwa Machida, Hiroko Shida, Masako Imanishi, Tadashi Grubbs, Brendan H. Hirasawa, Takeshi Mikami, Mikio |
author_sort | Hayashi, Masaru |
collection | PubMed |
description | Objectives: To conduct a comprehensive glycopeptide spectra analysis of serum between cancer and non-cancer patients to identify early biomarkers of epithelial ovarian cancer (EOC). Methods: Approximately 30,000 glycopeptide peaks were detected from the digested serum glycoproteins of 39 EOC patients (23 early-stage, 16 advanced-stage) and 45 non-cancer patients (27 leiomyoma and ovarian cyst cases, 18 endometrioma cases) by liquid chromatography mass spectrometry (LC–MS). The differential glycopeptide peak spectra were analyzed to distinguish between cancer and non-cancer groups by employing multivariate analysis including principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA) and heat maps. Results: Examined spectral peaks were filtered down to 2281 serum quantitative glycopeptide signatures for differentiation between ovarian cancer and controls using multivariate analysis. The OPLS-DA model using cross-validation parameters R2 and Q2 and score plots of the serum samples significantly differentiated the EOC group from the non-cancer control group. In addition, women with early-stage clear cell carcinoma and endometriomas were clearly distinguished from each other by OPLS-DA as well as by PCA and heat maps. Conclusions: Our study demonstrates the potential of comprehensive serum glycoprotein analysis as a useful tool for ovarian cancer detection. |
format | Online Article Text |
id | pubmed-6563019 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-65630192019-06-17 Comprehensive Serum Glycopeptide Spectra Analysis (CSGSA): A Potential New Tool for Early Detection of Ovarian Cancer Hayashi, Masaru Matsuo, Koji Tanabe, Kazuhiro Ikeda, Masae Miyazawa, Mariko Yasaka, Miwa Machida, Hiroko Shida, Masako Imanishi, Tadashi Grubbs, Brendan H. Hirasawa, Takeshi Mikami, Mikio Cancers (Basel) Article Objectives: To conduct a comprehensive glycopeptide spectra analysis of serum between cancer and non-cancer patients to identify early biomarkers of epithelial ovarian cancer (EOC). Methods: Approximately 30,000 glycopeptide peaks were detected from the digested serum glycoproteins of 39 EOC patients (23 early-stage, 16 advanced-stage) and 45 non-cancer patients (27 leiomyoma and ovarian cyst cases, 18 endometrioma cases) by liquid chromatography mass spectrometry (LC–MS). The differential glycopeptide peak spectra were analyzed to distinguish between cancer and non-cancer groups by employing multivariate analysis including principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA) and heat maps. Results: Examined spectral peaks were filtered down to 2281 serum quantitative glycopeptide signatures for differentiation between ovarian cancer and controls using multivariate analysis. The OPLS-DA model using cross-validation parameters R2 and Q2 and score plots of the serum samples significantly differentiated the EOC group from the non-cancer control group. In addition, women with early-stage clear cell carcinoma and endometriomas were clearly distinguished from each other by OPLS-DA as well as by PCA and heat maps. Conclusions: Our study demonstrates the potential of comprehensive serum glycoprotein analysis as a useful tool for ovarian cancer detection. MDPI 2019-04-27 /pmc/articles/PMC6563019/ /pubmed/31035594 http://dx.doi.org/10.3390/cancers11050591 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Hayashi, Masaru Matsuo, Koji Tanabe, Kazuhiro Ikeda, Masae Miyazawa, Mariko Yasaka, Miwa Machida, Hiroko Shida, Masako Imanishi, Tadashi Grubbs, Brendan H. Hirasawa, Takeshi Mikami, Mikio Comprehensive Serum Glycopeptide Spectra Analysis (CSGSA): A Potential New Tool for Early Detection of Ovarian Cancer |
title | Comprehensive Serum Glycopeptide Spectra Analysis (CSGSA): A Potential New Tool for Early Detection of Ovarian Cancer |
title_full | Comprehensive Serum Glycopeptide Spectra Analysis (CSGSA): A Potential New Tool for Early Detection of Ovarian Cancer |
title_fullStr | Comprehensive Serum Glycopeptide Spectra Analysis (CSGSA): A Potential New Tool for Early Detection of Ovarian Cancer |
title_full_unstemmed | Comprehensive Serum Glycopeptide Spectra Analysis (CSGSA): A Potential New Tool for Early Detection of Ovarian Cancer |
title_short | Comprehensive Serum Glycopeptide Spectra Analysis (CSGSA): A Potential New Tool for Early Detection of Ovarian Cancer |
title_sort | comprehensive serum glycopeptide spectra analysis (csgsa): a potential new tool for early detection of ovarian cancer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6563019/ https://www.ncbi.nlm.nih.gov/pubmed/31035594 http://dx.doi.org/10.3390/cancers11050591 |
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