The focus of this systematic review is to give an overview of the current status of clinical protein profiling studies using MALDI and SELDI MS platforms in the search for ovarian cancer biomarkers. A total of 34 profiling studies were qualified for inclusion in the review. Comparative analysis of published discriminatory peaks to peaks found in an original MALDI MS protein profiling study was made to address the key question of reproducibility across studies. An overlap was found despite substantial heterogeneity between studies relating to study design, biological material, pre-analytical treatment, and data analysis. About 47% of the peaks reported to be associated to ovarian cancer were also represented in our experimental study, and 34% of these redetected peaks also showed a significant difference between cases and controls in our study. Thus, despite known problems related to reproducibility an overlap in peaks between clinical studies was demonstrated, which indicate convergence toward a set of common discriminating, reproducible peaks for ovarian cancer. The potential of the discriminating protein peaks for clinical use as ovarian cancer biomarkers will be discussed and evaluated. This article is part of a Special Issue entitled: Proteomics: The clinical link.
"Of all the proteomic approaches used for this purpose , peptide and protein profiling has become a technique very close to clinical application . Peptide/protein profiling is based on the detection of discriminatory patterns that enable classifying and distinguishing between different study populations (e.g., patients versus healthy controls) and is currently being used as a clinical tool for the diagnosis of ovarian cancer , microbial typing  or MS imaging . The peptide/protein pattern is comprised of combinations of peaks in the mass spectra plot that are defined by the intensity of each signal on the y-axis, and the mass-to-charge ratio on the xaxis . "
[Show abstract][Hide abstract] ABSTRACT: The goal of this study was to investigate differences in tear peptidome/proteome profiles of aqueous-deficient dry eye, Meibomian gland dysfunction and control individuals. Tears from 93 individuals were collected by capillary and subjected to solid-phase extraction followed by MALDI-TOF for profiling analysis. Obtained spectra were aligned by variable penalty dynamic time warping (VPdtw) and the resulting data analyzed using multivariate statistics. Comparative analyses revealed good performance of VPdtw and a high discrimination of groups with a correct assignment of 89.3% using twelve informative peaks. SDS-PAGE followed by MALDI-TOF/TOF analysis allowed identification of lipocalin-1 as a biomarker candidate.
EuPA Open Proteomics 06/2014; 3. DOI:10.1016/j.euprot.2014.02.016
"They are, however, not very specific, although different insults may produce different patterns of acute phase response (APR). Many of the reported diagnostic SELDI peaks have been found to be acute phase proteins, and are described in several reviews [11,19,37,38]. SAA is primarily induced by pro-inflammatory cytokines such as IL-1β, TNF-α and IL-6, which are released by a variety of cells including activated tissue macrophages and blood monocytes in response to injury [39,40]. "
[Show abstract][Hide abstract] ABSTRACT: Classical scrapie in sheep is a fatal neurodegenerative disease associated with the conversion PrPC to PrPSc. Much is known about genetic susceptibility, uptake and dissemination of PrPSc in the body, but many aspects of prion diseases are still unknown. Different proteomic techniques have been used during the last decade to investigate differences in protein profiles between affected animals and healthy controls. We have investigated the protein profiles in serum of sheep with scrapie and healthy controls by SELDI-TOF-MS and LC-MS/MS. Latent Variable methods such as Principal Component Analysis, Partial Least Squares-Discriminant Analysis and Target Projection methods were used to describe the MS data.
The serum proteomic profiles showed variable differences between the groups both throughout the incubation period and at the clinical end stage of scrapie. At the end stage, the target projection model separated the two groups with a sensitivity of 97.8%, and serum amyloid A was identified as one of the protein peaks that differed significantly between the groups.
At the clinical end stage of classical scrapie, ten SELDI peaks significantly discriminated the scrapie group from the healthy controls. During the non-clinical incubation period, individual SELDI peaks were differently expressed between the groups at different time points. Investigations of differences in -omic profiles can contribute to new insights into the underlying disease processes and pathways, and advance our understanding of prion diseases, but comparison and validation across laboratories is difficult and challenging.
BMC Research Notes 11/2013; 6(1):466. DOI:10.1186/1756-0500-6-466
"A large number of review articles have appeared in the past several years, offering excellent overviews and perspectives on novel proteomic applications in cancer. Many reviews focused on different cancer types, such as breast cancer,1–4 pancreatic cancer,5,6 ovarian cancer,7–9 colorectal cancer,10,11 and glioma.12–14 Others have focused on sample types or subcellular components, such as tissue,15–17 serum,18–20 and secretome.21–23 "
[Show abstract][Hide abstract] ABSTRACT: Proteomic approaches are continuing to make headways in cancer research by helping to elucidate complex signaling networks that underlie tumorigenesis and disease progression. This review describes recent advances made in the proteomic discovery of drug targets for therapeutic development. A variety of technical and methodological advances are overviewed with a critical assessment of challenges and potentials. A number of potential drug targets, such as baculoviral inhibitor of apoptosis protein repeat-containing protein 6, macrophage inhibitory cytokine 1, phosphoglycerate mutase 1, prohibitin 1, fascin, and pyruvate kinase isozyme 2 were identified in the proteomic analysis of drug-resistant cancer cells, drug action, and differential disease state tissues. Future directions for proteomics-based target identification and validation to be more translation efficient are also discussed.
Drug Design, Development and Therapy 10/2013; 7:1259-1271. DOI:10.2147/DDDT.S52216 · 3.03 Impact Factor
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