Mining cancer biology through bioinformatic analysis of proteomic data.

Mining cancer biology through bioinformatic analysis of proteomic data.

Publication date: Aug 09, 2019

Introduction: Discovery proteomics for cancer research generates complex datasets of diagnostic, prognostic, and therapeutic significance in human cancer. With the advent of high resolution mass spectrometers, able to identify thousands of proteins in complex biological samples, only the application of bioinformatics can lead to the interpretation of data which can be relevant for cancer research. Areas covered: Here, we give an overview of the current bioinformatic tools used in cancer proteomics. Moreover, we describe their applications in cancer proteomics studies of cell lines, serum, and tissues, highlighting recent results and critically evaluating their outcomes. Expert opinion: The use of bioinformatic tools is a fundamental step in order to manage the large amount of proteins (from hundreds to thousands) that can be identified and quantified in a cancer biological samples by proteomics. To handle this challenge and obtain useful data for translational medicine, it is important the combined use of different bioinformatic tools. Moreover, a particular attention to the global experimental design, and the integration of multidisciplinary skills are essential for best setting of tool parameters and best interpretation of bioinformatics output.

Manfredi, M., Brandi, J., Di Carlo, C., Vanella, Vita, Barberis, E., Marengo, E., Patrone, M., and Cecconi, D. Mining cancer biology through bioinformatic analysis of proteomic data. 05160. 2019 Expert Rev Proteomics.

Concepts Keywords
Bioinformatic Genomics
Cancer Proteomics
Experimental Design Bioinformatics
Proteomic Mass spectrometry
Proteomics Proteomics
Serum
Translational Medicine

Semantics

Type Source Name
gene UNIPROT LARGE1
gene UNIPROT PTPN5
disease DOID cancer
disease MESH cancer

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