scholarly journals Proteomics Discovery of Disease Biomarkers

2008 ◽  
Vol 3 ◽  
pp. BMI.S689 ◽  
Author(s):  
Mamoun Ahram ◽  
Emanuel F. Petricoin
Keyword(s):  
Mass Spectrometry ◽  
Biomarker Discovery ◽  
Protein Microarrays ◽  
Proteomics Data ◽  
Disease Biomarkers ◽  
Surface Enhanced ◽  
Technological Developments ◽  
Novel Biomarkers ◽  
Analytical Processing ◽  
Multiple Samples

Recent technological developments in proteomics have shown promising initiatives in identifying novel biomarkers of various diseases. Such technologies are capable of investigating multiple samples and generating large amount of data end-points. Examples of two promising proteomics technologies are mass spectrometry, including an instrument based on surface enhanced laser desorption/ionization, and protein microarrays. Proteomics data must, however, undergo analytical processing using bioinformatics. Due to limitations in proteomics tools including shortcomings in bioinformatics analysis, predictive bioinformatics can be utilized as an alternative strategy prior to performing elaborate, high-throughput proteomics procedures. This review describes mass spectrometry, protein microarrays, and bioinformatics and their roles in biomarker discovery, and highlights the significance of integration between proteomics and bioinformatics.

Management and Analysis of Mass Spectrometry Proteomics Data on the Grid

2011 ◽  
pp. 206-227
Author(s):  
Mario Cannataro ◽  
Pietro Hiram Guzzi ◽  
Giuseppe Tradigo ◽  
Pierangelo Veltri
Keyword(s):  
Mass Spectrometry ◽  
Protein Identification ◽  
Biomarker Discovery ◽  
Integrated Management ◽  
Molecular Signature ◽  
Proteomics Data ◽  
Computational Proteomics ◽  
Manual Inspection ◽  
A Cell ◽  
Grid Based

Recent advances in high throughput technologies analysing biological samples enabled the researchers to collect a huge amount of data. In particular, mass spectrometry-based proteomics uses the mass spectrometry to investigate proteins expressed in an organism or a cell. The manual inspection of spectra is unfeasible, so the need to introduce a set of algorithms, tools and platforms to manage and analyze them arises. Computational Proteomics regards the computational methods for analyzing spectra data in qualitative (i.e. peptide/protein identification in tandem mass spectrometry), and quantitative proteomics (i.e. protein expression in samples), as well as in biomarker discovery (i.e. the identification of a molecular signature of a disease directly from spectra). This chapter presents main standards, tools, and technologies for building scalable, reusable, and portable applications in this field. The chapter surveys available solutions for computational proteomics and includes a deep description of MS-Analyzer, a Grid-based software platform for the integrated management and analysis of spectra data. MS-Analyzer provides efficient spectra management through a specialized spectra database, and supports the semantic composition of pre-processing and data mining services to analyze spectra on the Grid.

scholarly journals Extracellular Vesicle Proteome in Prostate Cancer: A Comparative Analysis of Mass Spectrometry Studies

2021 ◽  
Vol 22 (24) ◽  
pp. 13605
Author(s):  
Rui Miguel Marques Bernardino ◽  
Ricardo Leão ◽  
Rui Henrique ◽  
Luis Campos Pinheiro ◽  
Prashant Kumar ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Mass Spectrometry ◽  
Network Analysis ◽  
Extracellular Vesicles ◽  
Biomarker Discovery ◽  
Prostate Gland ◽  
Direct Interaction ◽  
Screening Methods ◽  
Proteomics Data ◽  
Discovery Research

Molecular diagnostics based on discovery research holds the promise of improving screening methods for prostate cancer (PCa). Furthermore, the congregated information prompts the question whether the urinary extracellular vesicles (uEV) proteome has been thoroughly explored, especially at the proteome level. In fact, most extracellular vesicles (EV) based biomarker studies have mainly targeted plasma or serum. Therefore, in this study, we aim to inquire about possible strategies for urinary biomarker discovery particularly focused on the proteome of urine EVs. Proteomics data deposited in the PRIDE archive were reanalyzed to target identifications of potential PCa markers. Network analysis of the markers proposed by different prostate cancer studies revealed moderate overlap. The recent throughput improvements in mass spectrometry together with the network analysis performed in this study, suggest that a larger standardized cohort may provide potential biomarkers that are able to fully characterize the heterogeneity of PCa. According to our analysis PCa studies based on urinary EV proteome presents higher protein coverage compared to plasma, plasma EV, and voided urine proteome. This together with a direct interaction of the prostate gland and urethra makes uEVs an attractive option for protein biomarker studies. In addition, urinary proteome based PCa studies must also evaluate samples from bladder and renal cancers to assess specificity for PCa.

scholarly journals The Bottleneck in the Cancer Biomarker Pipeline and Protein Quantification through Mass Spectrometry–Based Approaches: Current Strategies for Candidate Verification

2010 ◽  
Vol 56 (2) ◽  
pp. 212-222 ◽  
Author(s):  
Shalini Makawita ◽  
Eleftherios P Diamandis
Keyword(s):  
Mass Spectrometry ◽  
Biomarker Discovery ◽  
Multiple Reaction Monitoring ◽  
Protein Quantification ◽  
Great Promise ◽  
Limits Of Detection ◽  
Large Numbers ◽  
Target Analytes ◽  
Novel Biomarkers ◽  
Sample Purification

Abstract Background: Although robust discovery-phase platforms have resulted in the generation of large numbers of candidate cancer biomarkers, a comparable system for subsequent quantitative assessment and verification of all candidates is lacking. Established immunoassays and available antibodies permit analysis of small subsets of candidates; however, the lack of commercially available reagents, coupled with high costs and lengthy production and purification times, have rendered the large majority of candidates untestable. Content: Mass spectrometry (MS), and in particular multiple reaction monitoring (MRM)-MS, has emerged as an alternative technology to immunoassays for quantification of target proteins. Novel biomarkers are expected to be present in serum in the low (μg/L–ng/L) range, but analysis of complex serum or plasma digests by MS has yielded milligram per liter limits of detection at best. The coupling of prior sample purification strategies such as enrichment of target analytes, depletion of high-abundance proteins, and prefractionation, has enabled reliable penetration into the low microgram per liter range. This review highlights prospects for candidate verification through MS-based methods. We first outline the biomarker discovery pipeline and its existing bottleneck; we then discuss various MRM-based strategies for targeted protein quantification, the applicability of such methods for candidate verification, and points of concern. Summary: Although it is unlikely that MS-based protein quantification will replace immunoassays in the near future, with the expected improvements in limits of detection and specificity in instrumentation, MRM-based approaches show great promise for alleviating the existing bottleneck to discovery.

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