Cargando…

Quantitative proteomics characterization of cancer biomarkers and treatment

Cancer accounted for 16% of all death worldwide in 2018. Significant progress has been made in understanding tumor occurrence, progression, diagnosis, treatment, and prognosis at the molecular level. However, genomics changes cannot truly reflect the state of protein activity in the body due to the...

Descripción completa

Detalles Bibliográficos
Autores principales: Yang, Xiao-Li, Shi, Yi, Zhang, Dan-Dan, Xin, Rui, Deng, Jing, Wu, Ting-Miao, Wang, Hui-Min, Wang, Pei-Yao, Liu, Ji-Bin, Li, Wen, Ma, Yu-Shui, Fu, Da
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society of Gene & Cell Therapy 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8142045/
https://www.ncbi.nlm.nih.gov/pubmed/34095463
http://dx.doi.org/10.1016/j.omto.2021.04.006
_version_ 1783696495850029056
author Yang, Xiao-Li
Shi, Yi
Zhang, Dan-Dan
Xin, Rui
Deng, Jing
Wu, Ting-Miao
Wang, Hui-Min
Wang, Pei-Yao
Liu, Ji-Bin
Li, Wen
Ma, Yu-Shui
Fu, Da
author_facet Yang, Xiao-Li
Shi, Yi
Zhang, Dan-Dan
Xin, Rui
Deng, Jing
Wu, Ting-Miao
Wang, Hui-Min
Wang, Pei-Yao
Liu, Ji-Bin
Li, Wen
Ma, Yu-Shui
Fu, Da
author_sort Yang, Xiao-Li
collection PubMed
description Cancer accounted for 16% of all death worldwide in 2018. Significant progress has been made in understanding tumor occurrence, progression, diagnosis, treatment, and prognosis at the molecular level. However, genomics changes cannot truly reflect the state of protein activity in the body due to the poor correlation between genes and proteins. Quantitative proteomics, capable of quantifying the relatively different protein abundance in cancer patients, has been increasingly adopted in cancer research. Quantitative proteomics has great application potentials, including cancer diagnosis, personalized therapeutic drug selection, real-time therapeutic effects and toxicity evaluation, prognosis and drug resistance evaluation, and new therapeutic target discovery. In this review, the development, testing samples, and detection methods of quantitative proteomics are introduced. The biomarkers identified by quantitative proteomics for clinical diagnosis, prognosis, and drug resistance are reviewed. The challenges and prospects of quantitative proteomics for personalized medicine are also discussed.
format Online
Article
Text
id pubmed-8142045
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher American Society of Gene & Cell Therapy
record_format MEDLINE/PubMed
spelling pubmed-81420452021-06-03 Quantitative proteomics characterization of cancer biomarkers and treatment Yang, Xiao-Li Shi, Yi Zhang, Dan-Dan Xin, Rui Deng, Jing Wu, Ting-Miao Wang, Hui-Min Wang, Pei-Yao Liu, Ji-Bin Li, Wen Ma, Yu-Shui Fu, Da Mol Ther Oncolytics Review Cancer accounted for 16% of all death worldwide in 2018. Significant progress has been made in understanding tumor occurrence, progression, diagnosis, treatment, and prognosis at the molecular level. However, genomics changes cannot truly reflect the state of protein activity in the body due to the poor correlation between genes and proteins. Quantitative proteomics, capable of quantifying the relatively different protein abundance in cancer patients, has been increasingly adopted in cancer research. Quantitative proteomics has great application potentials, including cancer diagnosis, personalized therapeutic drug selection, real-time therapeutic effects and toxicity evaluation, prognosis and drug resistance evaluation, and new therapeutic target discovery. In this review, the development, testing samples, and detection methods of quantitative proteomics are introduced. The biomarkers identified by quantitative proteomics for clinical diagnosis, prognosis, and drug resistance are reviewed. The challenges and prospects of quantitative proteomics for personalized medicine are also discussed. American Society of Gene & Cell Therapy 2021-04-20 /pmc/articles/PMC8142045/ /pubmed/34095463 http://dx.doi.org/10.1016/j.omto.2021.04.006 Text en © 2021 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Yang, Xiao-Li
Shi, Yi
Zhang, Dan-Dan
Xin, Rui
Deng, Jing
Wu, Ting-Miao
Wang, Hui-Min
Wang, Pei-Yao
Liu, Ji-Bin
Li, Wen
Ma, Yu-Shui
Fu, Da
Quantitative proteomics characterization of cancer biomarkers and treatment
title Quantitative proteomics characterization of cancer biomarkers and treatment
title_full Quantitative proteomics characterization of cancer biomarkers and treatment
title_fullStr Quantitative proteomics characterization of cancer biomarkers and treatment
title_full_unstemmed Quantitative proteomics characterization of cancer biomarkers and treatment
title_short Quantitative proteomics characterization of cancer biomarkers and treatment
title_sort quantitative proteomics characterization of cancer biomarkers and treatment
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8142045/
https://www.ncbi.nlm.nih.gov/pubmed/34095463
http://dx.doi.org/10.1016/j.omto.2021.04.006
work_keys_str_mv AT yangxiaoli quantitativeproteomicscharacterizationofcancerbiomarkersandtreatment
AT shiyi quantitativeproteomicscharacterizationofcancerbiomarkersandtreatment
AT zhangdandan quantitativeproteomicscharacterizationofcancerbiomarkersandtreatment
AT xinrui quantitativeproteomicscharacterizationofcancerbiomarkersandtreatment
AT dengjing quantitativeproteomicscharacterizationofcancerbiomarkersandtreatment
AT wutingmiao quantitativeproteomicscharacterizationofcancerbiomarkersandtreatment
AT wanghuimin quantitativeproteomicscharacterizationofcancerbiomarkersandtreatment
AT wangpeiyao quantitativeproteomicscharacterizationofcancerbiomarkersandtreatment
AT liujibin quantitativeproteomicscharacterizationofcancerbiomarkersandtreatment
AT liwen quantitativeproteomicscharacterizationofcancerbiomarkersandtreatment
AT mayushui quantitativeproteomicscharacterizationofcancerbiomarkersandtreatment
AT fuda quantitativeproteomicscharacterizationofcancerbiomarkersandtreatment