Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps Carrillo Rodríguez, Paula Hernández Valladares, María del Carmen Mass spectrometry Proteomics Sample preparation Data-dependent acquisition (DDA) Data-independent acquisition (DIA) Workflows Data analysis Bioinformatics The qualitative and quantitative evaluation of proteome changes that condition cancer development can be achieved with liquid chromatography–mass spectrometry (LC-MS). LC-MSbased 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. 2023-02-20T08:54:04Z 2023-02-20T08:54:04Z 2023-01-16 journal article 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, 15, 555. [https://doi.org/10.3390/cancers15020555] https://hdl.handle.net/10481/80064 10.3390/cancers15020555 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional MDPI