141 research outputs found

    An integrative multi-platform analysis for discovering biomarkers of osteosarcoma

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    <p>Abstract</p> <p>Background</p> <p>SELDI-TOF-MS (Surface Enhanced Laser Desorption/Ionization-Time of Flight-Mass Spectrometry) has become an attractive approach for cancer biomarker discovery due to its ability to resolve low mass proteins and high-throughput capability. However, the analytes from mass spectrometry are described only by their mass-to-charge ratio (<it>m</it>/<it>z</it>) values without further identification and annotation. To discover potential biomarkers for early diagnosis of osteosarcoma, we designed an integrative workflow combining data sets from both SELDI-TOF-MS and gene microarray analysis.</p> <p>Methods</p> <p>After extracting the information for potential biomarkers from SELDI data and microarray analysis, their associations were further inferred by link-test to identify biomarkers that could likely be used for diagnosis. Immuno-blot analysis was then performed to examine whether the expression of the putative biomarkers were indeed altered in serum from patients with osteosarcoma.</p> <p>Results</p> <p>Six differentially expressed protein peaks with strong statistical significances were detected by SELDI-TOF-MS. Four of the proteins were up-regulated and two of them were down-regulated. Microarray analysis showed that, compared with an osteoblastic cell line, the expression of 653 genes was changed more than 2 folds in three osteosarcoma cell lines. While expression of 310 genes was increased, expression of the other 343 genes was decreased. The two sets of biomarkers candidates were combined by the link-test statistics, indicating that 13 genes were potential biomarkers for early diagnosis of osteosarcoma. Among these genes, cytochrome c1 (CYC-1) was selected for further experimental validation.</p> <p>Conclusion</p> <p>Link-test on datasets from both SELDI-TOF-MS and microarray high-throughput analysis can accelerate the identification of tumor biomarkers. The result confirmed that CYC-1 may be a promising biomarker for early diagnosis of osteosarcoma.</p

    The Use of Proteomic Technologies to Identify Serum Glycoproteins for the Early Detection of Liver and Prostate Cancers

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    The application of proteomic technologies to identify serum glycoproteins is an emerging technique to identify new biomarkers indicative of disease severity. Many of these newly evolving protein-profiling methodologies have evolved from previous global protein expression profiling studies such as those involving SELDI-TOF-MS technologies. Though the SELDI approach could distinguish disease from normal by utilizing protein patterns as shown herein with the HCC study of chapter II, it was unable to offer sequence information on the selected peaks, and did not have the ability to analyze the entire dynamic range of the serum/plasma proteome. To address these deficiencies, new strategies that incorporate the use of differential lectin-based glycoprotein capture and targeted immuno-based assays have been developed. The carbohydrate binding specificities of different lectins offers a biological affinity approach that both complements existing mass spectrometer capabilities and retains automated throughput options. A prostate cancer study using disease stratified samples is utilized herein to determine whether lectin capture can identify glycoproteins, which are indicative of different stages of prostate disease. By utilizing upfront lectin fractionation we show here evidence of glycoproteins and glycoprotein isoforms, which are specific to cancer progression. In addition, the incorporation of lectin fractionation followed by albumin depletion allows for a more in depth analysis of the entire dynamic range of the human serum and plasma proteome. Taken together we believe this approach is an attractive strategy for the discovery of proteins indicative of the early detection of liver and prostate cancers

    Application of Proteomics in Cancer Study

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    Cancer is one of the most malignant diseases in the world, accounting for 7.6 million deaths (around 13% of all deaths) in 2008 based on WHO reports. Early detection of cancer is vital due to its final control and prevention. Despite advances in diagnostic strategies, they have not the required sensitivity and specificity for prognosis. During the last decays, one of the most challenges for cancer research is to determine biological basis of this malignancy as a characteristic agents for an early-stage cancer. Understanding these agents requires molecular level examination of the disease followed by analysis of protein networks and their interactions in cells, signaling events among cancer cells, interactions among the cancer cells, and the tumor microenvironment. Proteomics as one of the modern areas of biochemistry holds great promise in cancer study. Inasmuch as, proteome reflects the real state of a cell, tissue or organism, it is expected to achieve more accurate tumor markers for disease diagnosis and therapeutic monitoring. In fact, the utility of this innovative large-scale proteome analyzer has shown significant prospective in biomarker discovery, patient monitoring, drug targeting and cell signaling; moreover, advances in the field of proteomics will provide new insight into the molecular complexity of the disease process, and enable the development of tools to help in treatment as well as in detection and prevention. In this review, proteomics approaches in cancer studies have been represented and discussed

    Methodology and Applications of Disease Biomarker Identification in Human Serum

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    Biomarkers are biomolecules that serve as indicators of biological and pathological processes, or physiological and pharmacological responses to a drug treatment. Because of the high abundance of albumin and heterogeneity of plasma lipoproteins and glycoproteins, biomarkers are difficult to identify in human serum. Due to the clinical significance the identification of disease biomarkers in serum holds great promise for personalized medicine, especially for disease diagnosis and prognosis. This review summarizes some common and emerging proteomics techniques utilized in the separation of serum samples and identification of disease signatures. The practical application of each protein separation or identification technique is analyzed using specific examples. Biomarkers of cancers of prostate, breast, ovary, and lung in human serum have been reviewed, as well as those of heart disease, arthritis, asthma, and cystic fibrosis. Despite the advancement of technology few biomarkers have been approved by the Food and Drug Administration for disease diagnosis and prognosis due to the complexity of structure and function of protein biomarkers and lack of high sensitivity, specificity, and reproducibility for those putative biomarkers. The combination of different types of technologies and statistical analysis may provide more effective methods to identify and validate new disease biomarkers in blood

    Challenges for Biomarker Discovery in Body Fluids Using SELDI-TOF-MS

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    Protein profiling using SELDI-TOF-MS has gained over the past few years an increasing interest in the field of biomarker discovery. The technology presents great potential if some parameters, such as sample handling, SELDI settings, and data analysis, are strictly controlled. Practical considerations to set up a robust and sensitive strategy for biomarker discovery are presented. This paper also reviews biological fluids generally available including a description of their peculiar properties and the preanalytical challenges inherent to sample collection and storage. Finally, some new insights for biomarker identification and validation challenges are provided

    A Bayesian framework for statistical signal processing and knowledge discovery in proteomic engineering

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    Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, February 2006.Includes bibliographical references (leaves 73-85).Proteomics has been revolutionized in the last couple of years through integration of new mass spectrometry technologies such as -Enhanced Laser Desorption/Ionization (SELDI) mass spectrometry. As data is generated in an increasingly rapid and automated manner, novel and application-specific computational methods will be needed to deal with all of this information. This work seeks to develop a Bayesian framework in mass-based proteomics for protein identification. Using the Bayesian framework in a statistical signal processing manner, mass spectrometry data is filtered and analyzed in order to estimate protein identity. This is done by a multi-stage process which compares probabilistic networks generated from mass spectrometry-based data with a mass-based network of protein interactions. In addition, such models can provide insight on features of existing models by identifying relevant proteins. This work finds that the search space of potential proteins can be reduced such that simple antibody-based tests can be used to validate protein identity. This is done with real proteins as a proof of concept. Regarding protein interaction networks, the largest human protein interaction meta-database was created as part of this project, containing over 162,000 interactions. A further contribution is the implementation of the massome network database of mass-based interactions- which is used in the protein identification process.(cont.) This network is explored in terms potential usefulness for protein identification. The framework provides an approach to a number of core issues in proteomics. Besides providing these tools, it yields a novel way to approach statistical signal processing problems in this domain in a way that can be adapted as proteomics-based technologies mature.by Gil Alterovitz.Ph.D

    Serum proteomic analysis of prostate cancer progression

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    Background: The reported incidence of prostate cancer (PCa) has increased in recent years due to the aging of the population and increased testing; however mortality rates have remained largely unchanged. Studies have shown deficiencies in predicting patient outcome for both of the major PCa diagnostic tools, namely prostate specific antigen (PSA) and trans rectal ultrasound ‐guided biopsy (TRUS). Therefore, serum biomarkers are needed that accurately predict prognosis of PCa (indolent vs. aggressive) and can thus inform clinical management. Aim: This study uses surface enhanced laser desorption/ionization time of flight mass spectrometry (SELDI‐TOF‐MS) analysis to identify differential serum protein expression between PCa patients with indolent vs. aggressive disease categorised by Gleason grade and biochemical recurrence. Materials and Methods: A total of 99 serum samples were selected for analysis. According to Gleason score, indolent (45 samples) and aggressive (54) forms of PCa were compared using univariate analysis. The same samples were then separated into groups of different recurrence status (10 metastatic, 15 biochemical recurrence and 70 nonrecurrences) and subjected to univariate analysis in the same way. The data from Gleason score and recurrence groups were then analysed using multivariate statistical analysis to improve PCa biomarker classification. Using gel‐electrophoresis technique, candidate biomarkers were separated and identified by LC‐MS/MS and validated using optimised Western blot (WB) immunoassay against 100 PCa serum samples from the Wales Cancer Bank (50 as indolent group & 50 as aggressive group). Results: The comparison between serum protein spectra from indolent and aggressive samples resulted in the identification of twenty‐six differentially expressed protein peaks (p<0.05), of which twenty proteins were found with 99% confidence. A total of 18 differentially expressed proteins (p<0.05) were found to distinguish between recurrence groups; three of these were robust with P<0.01. Sensitivity and specificity within the Gleason score group was 73.3% and 60% respectively and for the recurrence group 70% and 62.5%. Four candidate biomarkers (categorised by Gleason score) were identified using a novel 1 D LC‐MS/MS technique. The candidate biomarker with m/z of 9.3 kDa was found to be upregulated in aggressive PCa patients, and was identified as Apolipoprotein C‐I (ApoC‐I). Another three candidate biomarkers (22.2, 44.5 and 79.1 kDa) were found downregulated in the aggressive group and up‐ regulated in the indolent group and identified as apolipoprotein D (ApoD), putative uncharacterised protein (PUP) and Transferrin (TF), respectively. The utility of the putative biomarkers was examined by Western blot (WB) analysis of 100 blinded PCa serum samples. None of the three SELDI identified biomarkers were able to statistically identify PCa patients’ progression. Conclusion: The use of SELDI to identify potential PCa progression biomarkers has been confirmed in PCa patients. However, immunovalidation of prospective biomarkers in blinded PCa serum samples was unsuccessful. This study demonstrates the importance of validation in ascertaining the true clinical applicability of a cancer biomarker.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Applications of Mass Spectrometry in Proteomics and Pharmacokinetics

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    Tremendous technology improvements of the last decades has given mass spectrometry a more and more expanding role in the study of a wide range of molecules: from the identification and quantification of small molecular weight molecules to the structural determination of biomacromolecules. Many are the fields of application for this technique and the various versions of it. In the present study three different applications have been explored. The first application is a pharmacokinetics study of anticancer drug Gemcitabine and its principal metabolite, where the role of the LC-MS/MS is essential both for the selectivity of the detection of the small analytes and the sensitivity enhanced by multi-reaction monitoring experiments. The design of the study involved the collection of several blood samples at selected times and from patients that would have met certain eligibility criteria. The ESI demonstrated to be the most suitable approach and it provided the necessary data to conclude that toxicity of Gemcitabine did not increase when administered at FDR (Fixed Dose Rate) infusion in patients with impaired hepatic function. The second application describes an example of how MS represents a powerful tool in cancer research, from serum profiling study with high resolution MALDITOF and bioinformatic analysis, to the identification of potential biomarker through peak identification. Almost 400 serum sample – homogeneously distributed between biopsy confirmed ovarian cancer and high risk serum samples – were analyzed on a high resolution MALDI-TOF instrument after automated reverse phase magnetic beads separation. The high throughput data have undergone sophisticated bioinformatic procedures that lead to a list of upand down-regulated peaks, although identification studies were possible only for those peaks that showed a good reproducibility. One down-regolated peak has been identified using the LC-MS/MS technique. The identified peak confirmed a basic role of fibrinogen in the ovarian cancer; the other four peaks that have been identified as down-regulated showed an absolutely not satisfactory ionization in electro-spray, therefore further analysis will be performed on these analytes in order to determinate their amino acidic sequence. The most suitable technique seems to be MALDI-TOF/TOF mass spectrometry, since the peptides already showed a good degree of ionization in MALDI. The third and last study belongs to a quite new field, which is the combination of immuno precipitation assays with MALDI-TOF (Immuno Precipitation Mass Spectrometry, IPMS) experiments in order to evaluate the specificity of a series of monoclonal antibodies to specific antigen. The automated assay that has been developed provides structural information about the antigen that binds the monoclonal antibody to be tested and previously conjugated to the surface of magnetic beads, ideal support for robotic automation. IPMS showed its potential as a complementary tool of crucial importance in the selection of the monoclonal antibody for the development of ELISA based assay to be applied in the screening of a consistent number of human specimens for the clinical validation of proteins indicated in literature as potential biomarkers. Mass spectrometry in association with fractionation techniques, such as liquid or magnetic beads chromatography, is a very flexible tool in the cancer research field. Further improvement in the instrumentation and in the technology will bring always more and more results to be confident in

    Applications of Mass Spectrometry in Proteomics and Pharmacokinetics

    Get PDF
    Tremendous technology improvements of the last decades has given mass spectrometry a more and more expanding role in the study of a wide range of molecules: from the identification and quantification of small molecular weight molecules to the structural determination of biomacromolecules. Many are the fields of application for this technique and the various versions of it. In the present study three different applications have been explored. The first application is a pharmacokinetics study of anticancer drug Gemcitabine and its principal metabolite, where the role of the LC-MS/MS is essential both for the selectivity of the detection of the small analytes and the sensitivity enhanced by multi-reaction monitoring experiments. The design of the study involved the collection of several blood samples at selected times and from patients that would have met certain eligibility criteria. The ESI demonstrated to be the most suitable approach and it provided the necessary data to conclude that toxicity of Gemcitabine did not increase when administered at FDR (Fixed Dose Rate) infusion in patients with impaired hepatic function. The second application describes an example of how MS represents a powerful tool in cancer research, from serum profiling study with high resolution MALDITOF and bioinformatic analysis, to the identification of potential biomarker through peak identification. Almost 400 serum sample – homogeneously distributed between biopsy confirmed ovarian cancer and high risk serum samples – were analyzed on a high resolution MALDI-TOF instrument after automated reverse phase magnetic beads separation. The high throughput data have undergone sophisticated bioinformatic procedures that lead to a list of upand down-regulated peaks, although identification studies were possible only for those peaks that showed a good reproducibility. One down-regolated peak has been identified using the LC-MS/MS technique. The identified peak confirmed a basic role of fibrinogen in the ovarian cancer; the other four peaks that have been identified as down-regulated showed an absolutely not satisfactory ionization in electro-spray, therefore further analysis will be performed on these analytes in order to determinate their amino acidic sequence. The most suitable technique seems to be MALDI-TOF/TOF mass spectrometry, since the peptides already showed a good degree of ionization in MALDI. The third and last study belongs to a quite new field, which is the combination of immuno precipitation assays with MALDI-TOF (Immuno Precipitation Mass Spectrometry, IPMS) experiments in order to evaluate the specificity of a series of monoclonal antibodies to specific antigen. The automated assay that has been developed provides structural information about the antigen that binds the monoclonal antibody to be tested and previously conjugated to the surface of magnetic beads, ideal support for robotic automation. IPMS showed its potential as a complementary tool of crucial importance in the selection of the monoclonal antibody for the development of ELISA based assay to be applied in the screening of a consistent number of human specimens for the clinical validation of proteins indicated in literature as potential biomarkers. Mass spectrometry in association with fractionation techniques, such as liquid or magnetic beads chromatography, is a very flexible tool in the cancer research field. Further improvement in the instrumentation and in the technology will bring always more and more results to be confident in
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