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Latosinska A, Makridakis M, Frantzi M, Borràs DM, Janssen B, Mullen W, Zoidakis J, Merseburger AS, Jankowski V, Mischak H, Vlahou A. Integrative analysis of extracellular and intracellular bladder cancer cell line proteome with transcriptome: improving coverage and validity of -omics findings. Sci Rep 2016; 6:25619. [PMID: 27167498 PMCID: PMC4863247 DOI: 10.1038/srep25619] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Accepted: 04/18/2016] [Indexed: 01/23/2023] Open
Abstract
Characterization of disease-associated proteins improves our understanding of disease pathophysiology. Obtaining a comprehensive coverage of the proteome is challenging, mainly due to limited statistical power and an inability to verify hundreds of putative biomarkers. In an effort to address these issues, we investigated the value of parallel analysis of compartment-specific proteomes with an assessment of findings by cross-strategy and cross-omics (proteomics-transcriptomics) agreement. The validity of the individual datasets and of a “verified” dataset based on cross-strategy/omics agreement was defined following their comparison with published literature. The proteomic analysis of the cell extract, Endoplasmic Reticulum/Golgi apparatus and conditioned medium of T24 vs. its metastatic subclone T24M bladder cancer cells allowed the identification of 253, 217 and 256 significant changes, respectively. Integration of these findings with transcriptomics resulted in 253 “verified” proteins based on the agreement of at least 2 strategies. This approach revealed findings of higher validity, as supported by a higher level of agreement in the literature data than those of individual datasets. As an example, the coverage and shortlisting of targets in the IL-8 signalling pathway are discussed. Collectively, an integrative analysis appears a safer way to evaluate -omics datasets and ultimately generate models from valid observations.
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Affiliation(s)
- Agnieszka Latosinska
- Biotechnology Division, Biomedical Research Foundation of the Academy of Athens, Athens, Greece.,Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Manousos Makridakis
- Biotechnology Division, Biomedical Research Foundation of the Academy of Athens, Athens, Greece
| | | | - Daniel M Borràs
- GenomeScan B.V., Leiden, The Netherlands.,Institut National de la Santé et de la Recherche Médicale (INSERM), Institut of Cardiovascular and Metabolic Disease, Toulouse, France.,Université Toulouse III Paul-Sabatier, Toulouse, France
| | | | - William Mullen
- BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, United Kingdom
| | - Jerome Zoidakis
- Biotechnology Division, Biomedical Research Foundation of the Academy of Athens, Athens, Greece
| | - Axel S Merseburger
- Department of Urology, University of Lübeck, Lübeck, Germany.,Department of Urology and Urological Oncology, Hannover Medical School, Hannover, Germany
| | - Vera Jankowski
- RWTH-Aachen, Institute for Molecular Cardiovascular Research (IMCAR), Aachen, Germany
| | - Harald Mischak
- Mosaiques Diagnostics GmbH, Hannover, Germany.,BHF Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, United Kingdom
| | - Antonia Vlahou
- Biotechnology Division, Biomedical Research Foundation of the Academy of Athens, Athens, Greece
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