Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps.
bioinformatics
data analysis
data-dependent acquisition (DDA)
data-independent acquisition (DIA)
mass spectrometry
proteomics
sample preparation
workflows
Journal
Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829
Informations de publication
Date de publication:
16 Jan 2023
16 Jan 2023
Historique:
received:
04
01
2023
accepted:
12
01
2023
entrez:
21
1
2023
pubmed:
22
1
2023
medline:
22
1
2023
Statut:
epublish
Résumé
The qualitative and quantitative evaluation of proteome changes that condition cancer development can be achieved with liquid chromatography-mass spectrometry (LC-MS). LC-MS-based proteomics strategies are carried out according to predesigned workflows that comprise several steps such as sample selection, sample processing including labeling, MS acquisition methods, statistical treatment, and bioinformatics to understand the biological meaning of the findings and set predictive classifiers. As the choice of best options might not be straightforward, we herein review and assess past and current proteomics approaches for the discovery of new cancer biomarkers. Moreover, we review major bioinformatics tools for interpreting and visualizing proteomics results and suggest the most popular machine learning techniques for the selection of predictive biomarkers. Finally, we consider the approximation of proteomics strategies for clinical diagnosis and prognosis by discussing current barriers and proposals to circumvent them.
Identifiants
pubmed: 36672506
pii: cancers15020555
doi: 10.3390/cancers15020555
pmc: PMC9856946
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Subventions
Organisme : The Research Council of Norway
ID : 295910
Déclaration de conflit d'intérêts
The authors declare no conflict of interest.
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