Journal of Proteomics 2016 February 24 [Epub ahead of print] [Link]
Manfredi M, Martinotti S, Gosetti F, Ranzato E, Marengo E.
The secretome is the complex set of molecules secreted by cells; these molecules play a key role in cell signaling, communication and migration. Secretomics has been already used to discover new potential diagnostic biomarkers and therapeutic agents and to elucidate key autocrine pathways. Malignant mesothelioma (MMe), an extremely aggressive tumor, is characterized by a long latency period (20-30years), a poor prognosis, and limited effective therapies. MMe has a highly secretory cell type, and the factors released by cells may act in an autocrine or paracrine fashion on tumor and stroma, where they may modulate the extracellular environment. The aim of this work is to characterize the secretome of two MMe cell lines, MM98 and REN, in comparison with a mesothelial cell line Met5A, in order to evaluate differences and similarities of these two different MMe cancer model systems, and to identify potential biomarkers. We performed quantitative shotgun proteomics using SWATH-MS technology and we identified a total of 421 proteins, 112 expressed in the secretome of REN cells, 208 expressed in the secretome of MM98 cells and 189 secreted by mesothelial cells; 25 proteins are shared by the two mesothelioma cell lines.
This study characterizes the secretome signature of the REN and MM98 cell lines, confirming the availability of a cell-culture based model in order to describe the cell-specific properties, and to provide a list of putative cancer biomarkers. This work constitutes the first qualitative and quantitative proteomic approach performed on MMe secretome. Moreover, since the data were acquired in SWATH-MS acquisition mode, they can be successively re-mined without performing a new analysis of the sample, which is extremely useful for retrospective analyses. The overall aim was to identify novel tumor-derived protein biomarkers with the potential to be applied for early diagnosis, prognosis, therapy prediction and/or disease monitoring of MMe.