Mass spectrometry-based proteomics workflows in cancer research: the relevance of choosing the right steps. Mass Spec Methods

ABSTRACT: The qualitative and quantitative description of proteome changes that condition cancer development can be achieved by liquid chromatography-mass spectrometry (LC-MS). LC-MS-based proteomics strategies are carried out according to predesigned workflows that comprise of several steps such as sample selection, sample processing including labelling, 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 will herein review and assess past and current proteomics approaches for the discovery of new cancer biomarkers. While some aspects of the workflows might be subjected to sample type and size, we recommend, based on our experience and on others, the use of bead-based sample preparation followed by data-independent acquisition (DIA) with label-free quantification methodologies or data-dependent acquisition (DDA) with tandem mass tags (TMT) quantification for broad proteome and phosphoproteome characterizations. Moreover, we will review major bioinformatics tools to interpret and visualize proteomics results and suggest most popular machine learning techniques for the selection of predictive biomarkers. Finally, we would like the reader of this manuscript to reflect on the approximation of proteomics strategies to clinical diagnosis and prognosis by discussing current barriers and proposals to circumvent them

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cell Culture

SUBMITTER: Paula Carrillo Rodríguez  

LAB HEAD: María del Carmen Hernández Valladares

PROVIDER: PXD037656 | Pride | 2026-02-16

REPOSITORIES: Pride