Dataset Information

Differential membrane proteomics using 18O-labeling to identify biomarkers for cholangiocarcinoma.

ABSTRACT: Quantitative proteomic methodologies allow profiling of hundreds to thousands of proteins in a high-throughput fashion. This approach is increasingly applied to cancer biomarker discovery to identify proteins that are differentially regulated in cancers. Fractionation of protein samples based on enrichment of cellular subproteomes prior to mass spectrometric analysis can provide increased coverage of certain classes of molecules. We used a membrane protein enrichment strategy coupled with 18O labeling based quantitative proteomics to identify proteins that are highly expressed in cholangiocarcinomas. In addition to identifying several proteins previously known to be overexpressed in cholangiocarcinoma, we discovered a number of molecules that were previously not associated with cholangiocarcinoma. Using immunoblotting and immunohistochemical labeling of tissue microarrays, we validated Golgi membrane protein 1, Annexin IV and Epidermal growth factor receptor pathway substrate 8 (EPS8) as candidate biomarkers for cholangiocarcinomas. Golgi membrane protein 1 was observed to be overexpressed in 89% of cholangiocarcinoma cases analyzed by staining tissue microarrays. In light of recent reports showing that Golgi membrane protein 1 is detectable in serum, further investigation into validation of this protein has the potential to provide a biomarker for early detection of cholangiocarcinomas.

SUBMITTER: Kristiansen TZ

PROVIDER: S-EPMC3204659 | biostudies-literature | 2008 Nov

REPOSITORIES: biostudies-literature

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Differential membrane proteomics using 18O-labeling to identify biomarkers for cholangiocarcinoma.

Kristiansen Troels Zakarias TZ Harsha H C HC Grønborg Mads M Maitra Anirban A Pandey Akhilesh A

Journal of proteome research 20081008 11

Quantitative proteomic methodologies allow profiling of hundreds to thousands of proteins in a high-throughput fashion. This approach is increasingly applied to cancer biomarker discovery to identify proteins that are differentially regulated in cancers. Fractionation of protein samples based on enrichment of cellular subproteomes prior to mass spectrometric analysis can provide increased coverage of certain classes of molecules. We used a membrane protein enrichment strategy coupled with 18O la ...[more]

PMID: 18839982

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Project description:BackgroundQuantitative measurements of specific protein phosphorylation sites, as presented here, can be used to investigate signal transduction pathways, which is an important aspect of cell dynamics. The presented method quantitatively compares peptide abundances from experiments using 18O/16O labeling starting from elaborated MS spectra. It was originally developed to study signaling cascades activated by amyloid-beta treatment of neurons used as a cellular model system with relevance to Alzheimer's disease, but is generally applicable.ResultsThe presented method assesses, in complete cell lysates, the degree of phosphorylation of specific peptide residues from MS spectra using 18O/16O labeling. The abundance of each observed phospho-peptide from two cell states was estimated from three overlapping isotope contours. The influence of peptide-specific labeling efficiency was removed by performing a label swapped experiment and assuming that the labeling efficiency was unchanged upon label swapping. Different degrees of phosphorylation were reported using the fold change measure which was extended with a confidence interval found to reflect the quality of the underlying spectra. Furthermore a new way of method assessment using simulated data is presented. Using simulated data generated in a manner mimicking real data it was possible to show the method's robustness both with increasing noise levels and with decreasing labeling efficiency.ConclusionThe fold change error assessable on simulated data was on average 0.16 (median 0.10) with an error-to-signal ratio and labeling efficiency distributions similar to the ones found in the experimentally observed spectra. Applied to experimentally observed spectra a very good match was found to the model (<10% error for 85% of spectra) with a high degree of robustness, as assessed by data removal. This new method can thus be used for quantitative signal cascade analysis of total cell extracts in a high throughput mode.

Project description:Background:Cholangiocarcinoma (CCA) is an invasive malignancy arising from biliary epithelial cells; it is the most common primary tumour of the bile tract and has a poor prognosis. The aim of this study was to screen prognostic biomarkers for CCA by integrated multiomics analysis. Methods:The GSE32225 dataset was derived from the Gene Expression Omnibus (GEO) database and comprehensively analysed by using R software and The Cancer Genome Atlas (TCGA) database to obtain the differentially expressed RNAs (DERNAs) associated with CCA prognosis. Quantitative isobaric tags for relative and absolute quantification (iTRAQ) proteomics was used to screen differentially expressed proteins (DEPs) between CCA and nontumour tissues. Through integrated analysis of DERNA and DEP data, we obtained candidate proteins APOF, ITGAV and CASK, and immunohistochemistry was used to detect the expression of these proteins in CCA. The relationship between CASK expression and CCA prognosis was further analysed. Results:Through bioinformatics analysis, 875 DERNAs were identified, of which 10 were associated with the prognosis of the CCA patients. A total of 487 DEPs were obtained by using the iTRAQ technique. Comprehensive analysis of multiomics data showed that CASK, ITGAV and APOF expression at both the mRNA and protein levels were different in CCA compared with nontumour tissues. CASK was found to be expressed in the cytoplasm and nucleus of CCA cells in 38 (45%) of 84 patients with CCA. Our results suggested that patients with positive CASK expression had significantly better overall survival (OS) and recurrence-free survival (RFS) than those with negative CASK expression. Univariate and multivariate analyses demonstrated that negative expression of CASK was a significantly independent risk factor for OS and RFS in CCA patients. Conclusions:CASK may be a tumour suppressor; its low expression is an independent risk factor for a poor prognosis in CCA patients, and so it could be used as a clinically valuable prognostic marker.

Dataset Information

Differential membrane proteomics using 18O-labeling to identify biomarkers for cholangiocarcinoma.

Publications

Differential membrane proteomics using 18O-labeling to identify biomarkers for cholangiocarcinoma.

Similar Datasets

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