dc.contributor
Institut Català de la Salut
dc.contributor
[Carrillo-Rodriguez P] Proteomics Unit of University of Bergen (PROBE), University of Bergen, Bergen, Norway. Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain. [Selheim F] Proteomics Unit of University of Bergen (PROBE), University of Bergen, Bergen, Norway. [Hernandez-Valladares M] Proteomics Unit of University of Bergen (PROBE), University of Bergen, Bergen, Norway. Department of Physical Chemistry, University of Granada, Granada, Spain. Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain
dc.contributor
Vall d'Hebron Barcelona Hospital Campus
dc.contributor.author
Carrillo Rodriguez, Paula
dc.contributor.author
Selheim, Frode
dc.contributor.author
Hernandez-Valladares, Maria del Carmen
dc.date.accessioned
2025-10-24T09:34:19Z
dc.date.available
2025-10-24T09:34:19Z
dc.date.issued
2023-03-01T11:48:36Z
dc.date.issued
2023-03-01T11:48:36Z
dc.date.issued
2023-01-16
dc.identifier
Carrillo-Rodriguez P, Selheim F, Hernandez-Valladares M. Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps. Cancers. 2023 Jan 16;15(2):555.
dc.identifier
https://hdl.handle.net/11351/9084
dc.identifier
10.3390/cancers15020555
dc.identifier
000914823300001
dc.identifier.uri
http://hdl.handle.net/11351/9084
dc.description.abstract
Mass spectrometry; Proteomics; Workflows
dc.description.abstract
Espectrometría de masas; Proteómica; Flujos de trabajo
dc.description.abstract
Espectrometria de masses; Proteòmica; Fluxos de treball
dc.description.abstract
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.
dc.description.abstract
This research was funded by the Research Council of Norway INFRASTRUKTUR-program (project number: 295910).
dc.format
application/pdf
dc.relation
Cancers;15(2)
dc.relation
https://doi.org/10.3390/cancers15020555
dc.rights
Attribution 4.0 International
dc.rights
http://creativecommons.org/licenses/by/4.0/
dc.rights
info:eu-repo/semantics/openAccess
dc.subject
Espectrometria de masses
dc.subject
ANALYTICAL, DIAGNOSTIC AND THERAPEUTIC TECHNIQUES, AND EQUIPMENT::Investigative Techniques::Chemistry Techniques, Analytical::Mass Spectrometry::Tandem Mass Spectrometry
dc.subject
DISEASES::Neoplasms
dc.subject
DISCIPLINES AND OCCUPATIONS::Natural Science Disciplines::Biological Science Disciplines::Biochemistry::Proteomics
dc.subject
TÉCNICAS Y EQUIPOS ANALÍTICOS, DIAGNÓSTICOS Y TERAPÉUTICOS::técnicas de investigación::técnicas de química analítica::espectrometría de masas::espectrometría de masas en tándem
dc.subject
ENFERMEDADES::neoplasias
dc.subject
DISCIPLINAS Y OCUPACIONES::disciplinas de las ciencias naturales::disciplinas de las ciencias biológicas::bioquímica::proteómica
dc.title
Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps
dc.type
info:eu-repo/semantics/article
dc.type
info:eu-repo/semantics/publishedVersion
