Gene/Protein Disease Symptom Drug Enzyme Compound
Pivot Concepts:   Target Concepts:
Query: UNIPROT:P05109 (S100A8)
 1,212 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Pancreatic cancer is a rapidly fatal disease, and there is an urgent need for early detection markers and novel therapeutic targets. The current study has used a proteomic approach of two-dimensional (2D) gel electrophoresis and mass spectrometry (MS) to identify differentially expressed proteins in six cases of pancreatic adenocarcinoma, two normal adjacent tissues, seven cases of pancreatitis, and six normal pancreatic tissues. Protein extracts of individual sample and pooled samples of each type of tissues were separated on 2D gels using two different pH ranges. Differentially expressed protein spots were in-gel digested and identified by MS. Forty proteins were identified, of which five [i.e., alpha-amylase; copper zinc superoxide dismutase; protein disulfide isomerase, pancreatic; tropomyosin 2 (TM2); and galectin-1] had been associated previously with pancreatic disease in gene expression studies. The identified proteins include antioxidant enzymes, chaperones and/or chaperone-like proteins, calcium-binding proteins, proteases, signal transduction proteins, and extracellular matrix proteins. Among these proteins, annexin A4, cyclophilin A, cathepsin D, galectin-1, 14-3-3zeta, alpha-enolase, peroxiredoxin I, TM2, and S100A8 were specifically overexpressed in tumors compared with normal and pancreatitis tissues. Differential expression of some of the identified proteins was further confirmed by Western blot analyses and/or immunohistochemical analysis. These results show the value of a proteomic approach in identifying potential markers for early diagnosis and therapeutic manipulation. The newly identified proteins in pancreatic tumors may eventually serve as diagnostic markers or therapeutic targets.
 ...
PMID:Protein expression profiles in pancreatic adenocarcinoma compared with normal pancreatic tissue and tissue affected by pancreatitis as detected by two-dimensional gel electrophoresis and mass spectrometry. 1560 67

The proteins found in tears have an important role in the maintenance of the ocular surface and changes in the quality and quantity of tear components reflect changes in the health of the ocular surface. In this study, we have used quantitative proteomics, iTRAQ technology coupled with 2D-nanoLC-nano-ESI-MS/MS and with a statistical model to uncover proteins that are significantly and reliably changed in the tears of dry eye patients in an effort to reveal potential biomarker candidates. Fifty-six patients with dry eye and 40 healthy subjects were recruited for this study. In total, 93 tear proteins were identified with a ProtScore >or=2 (>or=99% confidence). Associated with dry eye were 6 up-regulated proteins, alpha-enolase, alpha-1-acid glycoprotein 1, S100 A8 (calgranulin A), S100 A9 (calgranulin B), S100 A4 and S100 A11 (calgizzarin) and 4 down-regulated proteins, prolactin-inducible protein (PIP), lipocalin-1, lactoferrin and lysozyme. Receiver operating curves (ROC) were evaluated for individual biomarker candidates and a biomarker panel. With the use of a 4-protein biomarker panel, the diagnostic accuracy for dry eye was 96% (sensitivity, 91.0%; specificity, 90.0%). Two biomarker candidates (alpha-enolase and S100 A4) generated from iTRAQ experiments were successfully verified using an ELISA assay. The levels of these 10 tear proteins reflect aqueous secretion deficiency by lacrimal gland, inflammatory status of the ocular surface. The clinical classification of the severity of the dry eye condition was successfully correlated to the proteomics by using three proteins that are associated with inflammation, alpha1-acid glycoprotein 1, S100 A8 and S100 A9. The nine tear protein biomarker candidates (except alpha1-acid glycoprotein 1) were also verified using an independent age-matched patient sample set. This study demonstrated that iTRAQ technology combined with 2D-nanoLC-nanoESI-MS/MS quantitative proteomics is a powerful tool for biomarker discovery.
 ...
PMID:Identification of tear fluid biomarkers in dry eye syndrome using iTRAQ quantitative proteomics. 1970 75