ArticleScientific reports2016
Integrative analysis of extracellular and intracellular bladder cancer cell line proteome with transcriptome: improving coverage and validity of -omics findings.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Article
- Molecular Changes in Tissue Proteome during Prostate Cancer Development: Proof-of-Principle Investigation.Diagnostics (Basel, Switzerland) · 2020Article
- Moesin (Cancers · 2020Article
- Urinary Proteomics in Predicting Heart Transplantation Outcomes (uPROPHET)-Rationale and database description.PloS one · 2017Article
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11 authors.
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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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