Dataset Information

Quantitative proteomic analysis for high-throughput screening of differential glycoproteins in hepatocellular carcinoma serum.

ABSTRACT: OBJECTIVE:Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths. Novel serum biomarkers are required to increase the sensitivity and specificity of serum screening for early HCC diagnosis. This study employed a quantitative proteomic strategy to analyze the differential expression of serum glycoproteins between HCC and normal control serum samples. METHODS:Lectin affinity chromatography (LAC) was used to enrich glycoproteins from the serum samples. Quantitative mass spectrometric analysis combined with stable isotope dimethyl labeling and 2D liquid chromatography (LC) separations were performed to examine the differential levels of the detected proteins between HCC and control serum samples. Western blot was used to analyze the differential expression levels of the three serum proteins. RESULTS:A total of 2,280 protein groups were identified in the serum samples from HCC patients by using the 2D LC-MS/MS method. Up to 36 proteins were up-regulated in the HCC serum, whereas 19 proteins were down-regulated. Three differential glycoproteins, namely, fibrinogen gamma chain (FGG), FOS-like antigen 2 (FOSL2), and ?-1,6-mannosylglycoprotein 6-?-N-acetylglucosaminyltransferase B (MGAT5B) were validated by Western blot. All these three proteins were up-regulated in the HCC serum samples. CONCLUSION:A quantitative glycoproteomic method was established and proven useful to determine potential novel biomarkers for HCC.

SUBMITTER: Gao HJ

PROVIDER: S-EPMC4607824 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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Quantitative proteomic analysis for high-throughput screening of differential glycoproteins in hepatocellular carcinoma serum.

Gao Hua-Jun HJ Chen Ya-Jing YJ Zuo Duo D Xiao Ming-Ming MM Li Ying Y Guo Hua H Zhang Ning N Chen Rui-Bing RB

Cancer biology & medicine 20150901 3

<h4>Objective</h4>Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths. Novel serum biomarkers are required to increase the sensitivity and specificity of serum screening for early HCC diagnosis. This study employed a quantitative proteomic strategy to analyze the differential expression of serum glycoproteins between HCC and normal control serum samples.<h4>Methods</h4>Lectin affinity chromatography (LAC) was used to enrich glycoproteins from the serum samples. Quantitativ ...[more]

PMID: 26487969

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Project description:BackgroundWe sought to discover serum biomarkers of ankylosing spondylitis (AS) for diagnosis and monitoring disease activity.MethodsWe studied biologic-treatment-naïve AS and healthy control (HC) patients' sera. Eighty samples matched by age, gender, and race (1:1:1 ratio) for AS patients with active disease, inactive disease, and HC were analyzed with SOMAscan™, an aptamer-based discovery platform. T-tests tests were performed for high/low-disease activity AS patients versus HCs (diagnosis) and high versus low disease activity (Monitoring) in a 2:1 and 1:1 ratio, respectively, to identify differentially expressed proteins (DEPs). We used the Cytoscape Molecular Complex Detection (MCODE) plugin to find clusters in protein-protein interaction networks and Ingenuity Pathway Analysis (IPA) for upstream regulators. Lasso regression analysis was performed for diagnosis.ResultsOf the 1317 proteins detected in our diagnosis and monitoring analyses, 367 and 167 (317 and 59, FDR-corrected q < .05) DEPs, respectively, were detected. MCODE identified complement, IL-10 signaling, and immune/interleukin signaling as the top 3 diagnosis PPI clusters. Complement, extracellular matrix organization/proteoglycans, and MAPK/RAS signaling were the top 3 monitoring PPI clusters. IPA showed interleukin 23/17 (interleukin 22, interleukin 23A), TNF (TNF receptor-associated factor 3), cGAS-STING (cyclic GMP-AMP synthase, Stimulator of Interferon Gene 1), and Jak/Stat (Signal transducer and activator of transcription 1), signaling in predicted upstream regulators. Lasso regression identified a Diagnostic 13-protein model predictive of AS. This model had a sensitivity of 0.75, specificity of 0.90, a kappa of 0.59, and overall accuracy of 0.80 (95% CI: 0.61-0.92). The AS vs HC ROC curve was 0.79 (95% CI: 0.61-0.96).ConclusionWe identified multiple candidate AS diagnostic and disease activity monitoring serum biomarkers using a comprehensive proteomic screen. Enrichment analysis identified key pathways in AS diagnosis and monitoring. Lasso regression identified a multi-protein panel with modest predictive ability.

Dataset Information

Quantitative proteomic analysis for high-throughput screening of differential glycoproteins in hepatocellular carcinoma serum.

Publications

Quantitative proteomic analysis for high-throughput screening of differential glycoproteins in hepatocellular carcinoma serum.

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