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Serum peptidome patterns of hepatocellular carcinoma based on magnetic bead separation and mass spectrometry analysis

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common cancers in the world,and the identification of biomarkers for the early detection is a relevant target. The purpose of the study is to discover specific low molecular weight (LMW) serum peptidome biomarkers and establish a diagnost...

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Autores principales: Ying, Xia, Han, Su-xia, Wang, Jun-lan, Zhou, Xia, Jin, Gui-hua, Jin, Long, Wang, Hao, Wu, Lei, Zhang, Jianying, Zhu, Qing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3751178/
https://www.ncbi.nlm.nih.gov/pubmed/23915185
http://dx.doi.org/10.1186/1746-1596-8-130
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author Ying, Xia
Han, Su-xia
Wang, Jun-lan
Zhou, Xia
Jin, Gui-hua
Jin, Long
Wang, Hao
Wu, Lei
Zhang, Jianying
Zhu, Qing
author_facet Ying, Xia
Han, Su-xia
Wang, Jun-lan
Zhou, Xia
Jin, Gui-hua
Jin, Long
Wang, Hao
Wu, Lei
Zhang, Jianying
Zhu, Qing
author_sort Ying, Xia
collection PubMed
description BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common cancers in the world,and the identification of biomarkers for the early detection is a relevant target. The purpose of the study is to discover specific low molecular weight (LMW) serum peptidome biomarkers and establish a diagnostic pattern for HCC. METHODS: We undertook this pilot study using a combined application of magnetic beads with Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) technique and ClinPro Tools v2.2 to detect 32 patients with HCC, 16 patients with chronic hepatitis (CH), 16 patients with liver cirrhosis (LC) and 16 healthy volunteers. RESULTS: The results showed 49, 33 and 37 differential peptide peaks respectively appeared in HCC, LC and CH groups. A Supervised Neural Network (SNN) algorithm was used to set up the classification model. Eleven of the identified peaks at m/z 5247.62, 7637.05, 1450.87, 4054.21, 1073.37, 3883.64, 5064.37, 4644.96, 5805.51, 1866.47 and 6579.6 were used to construct the peptides patterns. According to the model, we could clearly distinguish between HCC patients and healthy controls as well as between LC or CH patients and healthy controls. CONCLUSIONS: The study demonstrated that a combined application of magnetic beads with MALDI-TOF MB technique was suitable for identification of potential serum biomarkers for HCC and it is a promising way to establish a diagnostic pattern. VIRTUAL SLIDES: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1503629821958720.
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spelling pubmed-37511782013-08-24 Serum peptidome patterns of hepatocellular carcinoma based on magnetic bead separation and mass spectrometry analysis Ying, Xia Han, Su-xia Wang, Jun-lan Zhou, Xia Jin, Gui-hua Jin, Long Wang, Hao Wu, Lei Zhang, Jianying Zhu, Qing Diagn Pathol Research BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common cancers in the world,and the identification of biomarkers for the early detection is a relevant target. The purpose of the study is to discover specific low molecular weight (LMW) serum peptidome biomarkers and establish a diagnostic pattern for HCC. METHODS: We undertook this pilot study using a combined application of magnetic beads with Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) technique and ClinPro Tools v2.2 to detect 32 patients with HCC, 16 patients with chronic hepatitis (CH), 16 patients with liver cirrhosis (LC) and 16 healthy volunteers. RESULTS: The results showed 49, 33 and 37 differential peptide peaks respectively appeared in HCC, LC and CH groups. A Supervised Neural Network (SNN) algorithm was used to set up the classification model. Eleven of the identified peaks at m/z 5247.62, 7637.05, 1450.87, 4054.21, 1073.37, 3883.64, 5064.37, 4644.96, 5805.51, 1866.47 and 6579.6 were used to construct the peptides patterns. According to the model, we could clearly distinguish between HCC patients and healthy controls as well as between LC or CH patients and healthy controls. CONCLUSIONS: The study demonstrated that a combined application of magnetic beads with MALDI-TOF MB technique was suitable for identification of potential serum biomarkers for HCC and it is a promising way to establish a diagnostic pattern. VIRTUAL SLIDES: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1503629821958720. BioMed Central 2013-08-05 /pmc/articles/PMC3751178/ /pubmed/23915185 http://dx.doi.org/10.1186/1746-1596-8-130 Text en Copyright © 2013 Ying et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Ying, Xia
Han, Su-xia
Wang, Jun-lan
Zhou, Xia
Jin, Gui-hua
Jin, Long
Wang, Hao
Wu, Lei
Zhang, Jianying
Zhu, Qing
Serum peptidome patterns of hepatocellular carcinoma based on magnetic bead separation and mass spectrometry analysis
title Serum peptidome patterns of hepatocellular carcinoma based on magnetic bead separation and mass spectrometry analysis
title_full Serum peptidome patterns of hepatocellular carcinoma based on magnetic bead separation and mass spectrometry analysis
title_fullStr Serum peptidome patterns of hepatocellular carcinoma based on magnetic bead separation and mass spectrometry analysis
title_full_unstemmed Serum peptidome patterns of hepatocellular carcinoma based on magnetic bead separation and mass spectrometry analysis
title_short Serum peptidome patterns of hepatocellular carcinoma based on magnetic bead separation and mass spectrometry analysis
title_sort serum peptidome patterns of hepatocellular carcinoma based on magnetic bead separation and mass spectrometry analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3751178/
https://www.ncbi.nlm.nih.gov/pubmed/23915185
http://dx.doi.org/10.1186/1746-1596-8-130
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