Identification and Confirmation of Differentially Expressed Fucosylated Glycoproteins in the Serum of Ovarian Cancer Patients Using a Lectin Array and LC-MS/MS
ABSTRACT In order to discover potential glycoprotein biomarkers in ovarian cancer, we applied a lectin array and Exactag labeling based quantitative glycoproteomics approach. A lectin array strategy was used to detect overall lectin-specific glycosylation changes in serum proteins from patients with ovarian cancer and those with benign conditions. Lectins, which showed significant differential response for fucosylation, were used to extract glycoproteins that had been labeled using isobaric chemical tags. The glycoproteins were then identified and quantified by LC-MS/MS, and five glycoproteins were found to be differentially expressed in the serum of ovarian cancer patients compared to benign diseases. The differentially expressed glycoproteins were further confirmed by lectin-ELISA and ELISA assay. Corticosteroid-binding globulin (CBG), serum amyloid p component (SAP), complement factor B (CFAB), and histidine-rich glycoprotein (HRG) were identified as potential markers for differentiating ovarian cancer from benign diseases or healthy controls. A combination of CBG and HRG (AUC = 0.825) showed comparable performance to CA125 (AUC = 0.829) in differentiating early stage ovarian cancer from healthy controls. The combination of CBG, SAP, and CA125 showed improved performance for distinguishing stage III ovarian cancer from benign diseases compared to CA125 alone. The ability of CBG, SAP, HRG, and CFAB to differentiate the serum of ovarian cancer patients from that of controls was tested using an independent set of samples. Our findings suggest that glycoprotein modifications may be a means to identify novel diagnostic markers for detection of ovarian cancer.
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ABSTRACT: Aberrant glycosylation has been observed for decades in essentially all types of cancer, and is now well established as an indicator of carcinogenesis. Mining the glycome for biomarkers, however, requires analytical methods that can rapidly separate, identify, and quantify isomeric glycans. We have developed a rapid-throughput method for chromatographic glycan profiling using microfluidic chip-based nanoflow liquid chromatography (nano-LC)/mass spectrometry. To demonstrate the utility of this method, we analyzed and compared serum samples from epithelial ovarian cancer cases (n=46) and healthy control individuals (n=48). Over 250 N-linked glycan compound peaks with over 100 distinct N-linked glycan compositions were identified. Statistical testing identified 26 potential glycan biomarkers based on both compositional and structure-specific analyses. Using these results, an optimized model was created incorporating the combined abundances of seven potential glycan biomarkers. The receiver operating characteristic (ROC) curve of this optimized model had an area under the curve (AUC) of 0.96, indicating robust discrimination between cancer cases and healthy controls. Rapid-throughput chromatographic glycan profiling was found to be an effective platform for structure-specific biomarker discovery.Journal of Chromatography A 01/2013; 1279. DOI:10.1016/j.chroma.2012.12.079 · 4.26 Impact Factor
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ABSTRACT: Protein biomarkers have the potential to transform medicine as they are clinically used to diagnose diseases, stratify patients and follow disease states. Even though a large number of potential biomarkers have been proposed over the past few years, almost none of them have been so far implemented in the clinic. One of the reasons for this limited success is the lack of technologies to validate proposed biomarker candidates in larger patient cohorts. This limitation could be alleviated by the use of antibody-independent validation methods such as Selected Reaction Monitoring (SRM). Similar to measurements based on affinity-reagents, SRM based targeted mass spectrometry also requires the generation of definitive assays for each targeted analyte. Here we present a library of SRM assays for 5568 N-glycosites enabling the multiplexed evaluation of clinically relevant N-glycoproteins as biomarker candidates. We demonstrate that this resource can be utilized to select SRM assay sets for cancer-associated N-glycoproteins for their subsequent multiplexed and consistent quantification in 120 human plasma samples. We show that N-glycoproteins spanning five orders of magnitude in abundance can be quantified and that previously reported abundance differences in various cancer types can be recapitulated. Together, the established N-Glycoprotein SRMAtlas resource (available online at http://www.srmatlas.org/) facilitates parallel, efficient, consistent, and sensitive evaluation of proposed biomarker candidates in large clinical sample cohorts.Molecular & Cellular Proteomics 02/2013; DOI:10.1074/mcp.O112.026617 · 7.25 Impact Factor
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ABSTRACT: The current study evaluated the glycoproteomic profile of tissues from colon cancer patients. The lectin microarray was first performed to compare the glycoprotein profiles between colon cancer and matched normal tissues. Level of N-acetylglucosamine (GlcNAc) that Solanum tuberosum lectin (STL) bound was found to be elevated in colon cancer, which was verified through lectin histochemistry. The subsequent glycoproteomic analysis based on STL enrichment of glycoproteins followed by label-free quantitative nano liquid chromatography-mass spectrometry/mass spectrometry (nanoLC-MS/MS) analysis identified 72 proteins in high confidence. Among these proteins, 17 were exclusively detected in cancer tissues, and 14 were significantly upregulated in tumor tissues. Annexin A1 and HSP90β were chosen for further investigation by immunoprecipitation coupled with lectin blots, western blots and tissue microarrays. Both Annexin A1 and HSP90β were GlcNAcylated, and their protein expressions were elevated in colon cancer, compared to normal tissues. Moreover, specific changes of GlcNAc abundances in Annexin A1 and HSP90β suggested that tumor-specific glycan patterns could serve as candidate biomarkers of colon cancer for distinguishing cancer patients from healthy individuals.Molecular BioSystems 04/2013; 9(7). DOI:10.1039/c3mb00013c · 3.18 Impact Factor