Dataset Information

Protein profiling in hepatocellular carcinoma by label-free quantitative proteomics in two west African populations.

ABSTRACT: Hepatocellular Carcinoma is the third most common cause of cancer related death worldwide, often diagnosed by measuring serum AFP; a poor performance stand-alone biomarker. With the aim of improving on this, our study focuses on plasma proteins identified by Mass Spectrometry in order to investigate and validate differences seen in the respective proteomes of controls and subjects with LC and HCC.Mass Spectrometry analysis using liquid chromatography electro spray ionization quadrupole time-of-flight was conducted on 339 subjects using a pooled expression profiling approach. ELISA assays were performed on four significantly differentially expressed proteins to validate their expression profiles in subjects from the Gambia and a pilot group from Nigeria. Results from this were collated for statistical multiplexing using logistic regression analysis.Twenty-six proteins were identified as differentially expressed between the three subject groups. Direct measurements of four; hemopexin, alpha-1-antitrypsin, apolipoprotein A1 and complement component 3 confirmed their change in abundance in LC and HCC versus control patients. These trends were independently replicated in the pilot validation subjects from Nigeria. The statistical multiplexing of these proteins demonstrated performance comparable to or greater than ALT in identifying liver cirrhosis or carcinogenesis. This exercise also proposed preliminary cut offs with achievable sensitivity, specificity and AUC statistics greater than reported AFP averages.The validated changes of expression in these proteins have the potential for development into high-performance tests usable in the diagnosis and or monitoring of HCC and LC patients. The identification of sustained expression trends strengthens the suggestion of these four proteins as worthy candidates for further investigation in the context of liver disease. The statistical combinations also provide a novel inroad of analyses able to propose definitive cut-offs and combinations for evaluation of performance.

SUBMITTER: Fye HK

PROVIDER: S-EPMC3728326 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Protein profiling in hepatocellular carcinoma by label-free quantitative proteomics in two west African populations.

Fye Haddy K S HK Wright-Drakesmith Cynthia C Kramer Holger B HB Camey Suzi S Nogueira da Costa Andre A Jeng Adam A Bah Alasana A Kirk Gregory D GD Sharif Mohamed I F MI Ladep Nimzing G NG Okeke Edith E Hainaut Pierre P Taylor-Robinson Simon D SD Kessler Benedikt M BM Mendy Maimuna E ME

PloS one 20130730 7

<h4>Background</h4>Hepatocellular Carcinoma is the third most common cause of cancer related death worldwide, often diagnosed by measuring serum AFP; a poor performance stand-alone biomarker. With the aim of improving on this, our study focuses on plasma proteins identified by Mass Spectrometry in order to investigate and validate differences seen in the respective proteomes of controls and subjects with LC and HCC.<h4>Methods</h4>Mass Spectrometry analysis using liquid chromatography electro sp ...[more]

PMID: 23935864

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Project description:BackgroundLabel-free quantitative proteomics holds a great deal of promise for the future study of both medicine and biology. However, the data generated is extremely intricate in its correlation structure, and its proper analysis is complex. There are issues with missing identifications. There are high levels of correlation between many, but not all, of the peptides derived from the same protein. Additionally, there may be systematic shifts in the sensitivity of the machine between experiments or even through time within the duration of a single experiment.ResultsWe describe a hierarchical model for analyzing unbiased, label-free proteomics data which utilizes the covariance of peptide expression across samples as well as MS/MS-based identifications to group peptides-a strategy we call metaprotein expression modeling. Our metaprotein model acknowledges the possibility of misidentifications, post-translational modifications and systematic differences between samples due to changes in instrument sensitivity or differences in total protein concentration. In addition, our approach allows us to validate findings from unbiased, label-free proteomics experiments with further unbiased, label-free proteomics experiments. Finally, we demonstrate the clinical/translational utility of the model for building predictors capable of differentiating biological phenotypes as well as for validating those findings in the context of three novel cohorts of patients with Hepatitis C.ConclusionsMass-spectrometry proteomics is quickly becoming a powerful tool for studying biological and translational questions. Making use of all of the information contained in a particular set of data will be critical to the success of those endeavors. Our proposed model represents an advance in the ability of statistical models of proteomic data to identify and utilize correlation between features. This allows validation of predictors without translation to targeted assays in addition to informing the choice of targets when it is appropriate to generate those assays.

Project description:BackgroundRenal cell carcinoma is the most common form of kidney cancer which is a global threat to human health, needing to explore effective therapeutic targets and treatment methods. Aurora kinase B acts as an important carcinogenic role in various kinds of tumors, while its mechanism in renal cell carcinoma is indistinct. Herein we explore the underlying mechanism of Aurora kinase B in renal cell carcinoma.Methods and resultsLabel-free quantitative proteomics analysis was employed to analyze the differentially expressed proteins in 786-O cells which were treated with si-Aurora kinase B or si-ctrl. In the current study, 169 differentially expressed proteins were identified. The top 10 upregulated proteins were MX2, IFI44L, ISG20, DDX58, F3, IFI44, ECE1, PRIC285, NIT1, and IFIT2. The top 10 downregulated proteins were FKBP9, FSTL1, DDAH1, TGFB2, HMGN3, COIL, FAM65A, PTPN14, ARFGAP2, and EIF2C2. GO enrichment analysis showed that these differentially expressed proteins participated in biological processes, including defense response to virus, response to virus, and type I interferon signaling pathway. These differentially expressed proteins participated in cellular components, including focal adhesion, cell-substrate adherens junction, cell-substrate junction, and endoplasmic reticulum lumen. These differentially expressed proteins participated in molecule functions, including guanyl nucleotide binding, nucleotidase activity, double-stranded RNA binding, 2'-5'-oligoadenylate synthetase activity, and virus receptor activity. Kyoto Encyclopedia of Genes and Genomes pathway analysis showed that the significantly changed proteins including OAS3, OAS2, JAK1, TAP1, and RAC1 were involved in Epstein-Barr virus infection.ConclusionsTaken together, our results demonstrate the possible mechanisms that Aurora kinase B may participate in renal cell carcinoma. These findings may provide insights into tumorigenesis and a theoretical basis for developing potential therapies of renal cell carcinoma.

Project description:BackgroundAlthough a great deal of rice proteomic research has been conducted, there are relatively few studies specifically addressing the rice grain proteome. The existing rice grain proteomic researches have focused on the identification of differentially expressed proteins or monitoring protein expression patterns during grain filling stages.ResultsProteins were extracted from rice grains 10, 20, and 30 days after flowering, as well as from fully mature grains. By merging all of the identified proteins in this study, we identified 4,172 non-redundant proteins with a wide range of molecular weights (from 5.2 kDa to 611 kDa) and pI values (from pH 2.9 to pH 12.6). A Genome Ontology category enrichment analysis for the 4,172 proteins revealed that 52 categories were enriched, including the carbohydrate metabolic process, transport, localization, lipid metabolic process, and secondary metabolic process. The relative abundances of the 1,784 reproducibly identified proteins were compared to detect 484 differentially expressed proteins during rice grain development. Clustering analysis and Genome Ontology category enrichment analysis revealed that proteins involved in the metabolic process were enriched through all stages of development, suggesting that proteome changes occurred even in the desiccation phase. Interestingly, enrichments of proteins involved in protein folding were detected in the desiccation phase and in fully mature grain.ConclusionThis is the first report conducting comprehensive identification of rice grain proteins. With a label free shotgun proteomic approach, we identified large number of rice grain proteins and compared the expression patterns of reproducibly identified proteins during rice grain development. Clustering analysis, Genome Ontology category enrichment analysis, and the analysis of composite expression profiles revealed dynamic changes of metabolisms during rice grain development. Interestingly, we detected that proteins involved in glycolysis, TCA-cycle, lipid metabolism, and proteolysis accumulated at higher levels in fully mature grain compared to grain developing stages, suggesting that the accumulation of these proteins during the desiccation stage may be associated with the preparation of proteins required in germination.

Dataset Information

Protein profiling in hepatocellular carcinoma by label-free quantitative proteomics in two west African populations.

Publications

Protein profiling in hepatocellular carcinoma by label-free quantitative proteomics in two west African populations.

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