Bin Zhou1, Zhe Zhou2, Yuling Chen3, Haiteng Deng4, Yunlong Cai1, Xiaolong Rao1, Yuxin Yin5, Long Rong6. 1. Department of Endoscopy Center, Peking University First Hospital, Beijing, China. 2. Institute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China. 3. MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systematic Biology, School of Life Sciences, Tsinghua University, Beijing, China; Tsinghua University-Peking University Joint Center for Life Sciences, Beijing, China. 4. MOE Key Laboratory of Bioinformatics, Center for Synthetic and Systematic Biology, School of Life Sciences, Tsinghua University, Beijing, China. 5. Institute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China. Electronic address: yinyuxin@hsc.pku.edu.cn. 6. Department of Endoscopy Center, Peking University First Hospital, Beijing, China. Electronic address: ronglong8@vip.sina.com.
Abstract
BACKGROUND: Identification and treatment in the early stage can significantly improve the prognosis of gastric cancer (GC). However, to date, there is still no ideal biomarker that can be used for the screening of early stage GC (EGC). The proteomics supported by mass spectrometry offers more possibilities for discovering tumor biomarkers. The aim of this study was to explore candidate protein biomarkers for EGC screening with mass spectrometry and bioinformatics technology. METHODS: Plasma samples were collected from 15 EGC patients and 15 healthy controls. After a selective immune-depletion to remove high abundance proteins, plasma samples were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) combined with the tandem mass tags (TMT) labeling. RESULTS: A total of 2040 proteins were identified, and 11 proteins were found to be differentially expressed. The results of the logistic regression model and orthogonal signal correction-partial least squares discriminant analysis (OPLS-DA) model showed that the changed proteins identified by plasma proteomics could help distinguish EGC patients from healthy controls. CONCLUSION: The proteins identified by plasma proteomics using LC-MS/MS combined with TMT labeling could help distinguish EGC from healthy controls.
BACKGROUND: Identification and treatment in the early stage can significantly improve the prognosis of gastric cancer (GC). However, to date, there is still no ideal biomarker that can be used for the screening of early stage GC (EGC). The proteomics supported by mass spectrometry offers more possibilities for discovering tumor biomarkers. The aim of this study was to explore candidate protein biomarkers for EGC screening with mass spectrometry and bioinformatics technology. METHODS: Plasma samples were collected from 15 EGC patients and 15 healthy controls. After a selective immune-depletion to remove high abundance proteins, plasma samples were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) combined with the tandem mass tags (TMT) labeling. RESULTS: A total of 2040 proteins were identified, and 11 proteins were found to be differentially expressed. The results of the logistic regression model and orthogonal signal correction-partial least squares discriminant analysis (OPLS-DA) model showed that the changed proteins identified by plasma proteomics could help distinguish EGC patients from healthy controls. CONCLUSION: The proteins identified by plasma proteomics using LC-MS/MS combined with TMT labeling could help distinguish EGC from healthy controls.
Authors: Dan Shao; Lan Huang; Yan Wang; Xueteng Cui; Yufei Li; Yao Wang; Qin Ma; Wei Du; Juan Cui Journal: Database (Oxford) Date: 2021-10-13 Impact factor: 3.451
Authors: Michael W L Quirino; Amanda P B Albuquerque; Maria F D De Souza; Antônio F Da Silva Filho; Mário R Martins; Maira G Da Rocha Pitta; Michelly C Pereira; Moacyr J B De Melo Rêgo Journal: Eur J Histochem Date: 2022-09-29 Impact factor: 1.966
Authors: Marco Tognetti; Kamil Sklodowski; Sebastian Müller; Dominique Kamber; Jan Muntel; Roland Bruderer; Lukas Reiter Journal: J Proteome Res Date: 2022-05-23 Impact factor: 5.370