Advanced

Serum proteome profiling of pancreatitis using recombinant antibody microarrays reveals disease-associated biomarker signatures

Sandström Gerdtsson, Anna LU ; Andersson, Roland LU ; Segersvard, R.; Lohr, M.; Borrebaeck, Carl LU and Wingren, Christer LU (2012) In Proteomics Clinical Applications 6(9-10). p.96-486
Abstract
PURPOSE: Pancreatitis is an inflammatory state of the pancreas, for which high-performing serological biomarkers are lacking. The aim of the present study was to evaluate the use of affinity proteomics for identifying potential markers of disease and stratifying pancreatitis subtypes. EXPERIMENTAL DESIGN: High-content, recombinant antibody microarrays were applied for serum protein expression profiling of 113 serum samples from patients with chronic, acute, and autoimmune pancreatitis, as well as healthy controls. The sample groups were compared using supervised classification based on support vector machine analysis. RESULTS: This discovery study showed that pancreatitis subtypes could be discriminated with high accuracy. Using unfiltered... (More)
PURPOSE: Pancreatitis is an inflammatory state of the pancreas, for which high-performing serological biomarkers are lacking. The aim of the present study was to evaluate the use of affinity proteomics for identifying potential markers of disease and stratifying pancreatitis subtypes. EXPERIMENTAL DESIGN: High-content, recombinant antibody microarrays were applied for serum protein expression profiling of 113 serum samples from patients with chronic, acute, and autoimmune pancreatitis, as well as healthy controls. The sample groups were compared using supervised classification based on support vector machine analysis. RESULTS: This discovery study showed that pancreatitis subtypes could be discriminated with high accuracy. Using unfiltered data, the individual subtypes, as well as the combined pancreatitis cohort, were distinguished from healthy controls with high AUC values (0.96-1.00). Moreover, characteristic protein patterns and AUC values in the range of 0.69-0.95 were observed for the individual pancreatitis entities when compared to each other, and to all other samples combined. CONCLUSIONS AND CLINICAL RELEVANCE: This study demonstrated the potential of the antibody microarray approach for stratification of pancreatitis. Distinct candidate multiplex serum biomarker signatures for chronic, acute, and autoimmune pancreatitis were defined, which could enhance our fundamental knowledge of the underlying molecular mechanisms, and potentially lead to improved diagnosis. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Retrospective Studies, Proteomics, Proteome/*analysis, *Protein Array Analysis, Humans, Female, Gene Expression Profiling, 80 and over, Area Under Curve, Recombinant Proteins/genetics/immunology/metabolism, Biological Markers/blood, Adult, Aged, Adolescent, Male, Pancreatitis/*blood/pathology, Middle Aged, Single-Chain Antibodies/genetics/*immunology/metabolism, Young Adult
in
Proteomics Clinical Applications
volume
6
issue
9-10
pages
96 - 486
publisher
John Wiley & Sons
external identifiers
  • wos:000310565700006
  • pmid:22930578
  • scopus:84868115591
ISSN
1862-8354
DOI
10.1002/prca.201200051
project
CREATE Health
language
English
LU publication?
yes
id
a0e9691d-76b6-49f5-a6a9-e5e05eba493e (old id 4146670)
alternative location
http://www.ncbi.nlm.nih.gov/pubmed/22930578
date added to LUP
2013-11-19 10:27:51
date last changed
2017-07-09 04:32:52
@article{a0e9691d-76b6-49f5-a6a9-e5e05eba493e,
  abstract     = {PURPOSE: Pancreatitis is an inflammatory state of the pancreas, for which high-performing serological biomarkers are lacking. The aim of the present study was to evaluate the use of affinity proteomics for identifying potential markers of disease and stratifying pancreatitis subtypes. EXPERIMENTAL DESIGN: High-content, recombinant antibody microarrays were applied for serum protein expression profiling of 113 serum samples from patients with chronic, acute, and autoimmune pancreatitis, as well as healthy controls. The sample groups were compared using supervised classification based on support vector machine analysis. RESULTS: This discovery study showed that pancreatitis subtypes could be discriminated with high accuracy. Using unfiltered data, the individual subtypes, as well as the combined pancreatitis cohort, were distinguished from healthy controls with high AUC values (0.96-1.00). Moreover, characteristic protein patterns and AUC values in the range of 0.69-0.95 were observed for the individual pancreatitis entities when compared to each other, and to all other samples combined. CONCLUSIONS AND CLINICAL RELEVANCE: This study demonstrated the potential of the antibody microarray approach for stratification of pancreatitis. Distinct candidate multiplex serum biomarker signatures for chronic, acute, and autoimmune pancreatitis were defined, which could enhance our fundamental knowledge of the underlying molecular mechanisms, and potentially lead to improved diagnosis.},
  author       = {Sandström Gerdtsson, Anna and Andersson, Roland and Segersvard, R. and Lohr, M. and Borrebaeck, Carl and Wingren, Christer},
  issn         = {1862-8354},
  keyword      = {Retrospective Studies,Proteomics,Proteome/*analysis,*Protein Array Analysis,Humans,Female,Gene Expression Profiling,80 and over,Area Under Curve,Recombinant Proteins/genetics/immunology/metabolism,Biological Markers/blood,Adult,Aged,Adolescent,Male,Pancreatitis/*blood/pathology,Middle Aged,Single-Chain Antibodies/genetics/*immunology/metabolism,Young Adult},
  language     = {eng},
  number       = {9-10},
  pages        = {96--486},
  publisher    = {John Wiley & Sons},
  series       = {Proteomics Clinical Applications},
  title        = {Serum proteome profiling of pancreatitis using recombinant antibody microarrays reveals disease-associated biomarker signatures},
  url          = {http://dx.doi.org/10.1002/prca.201200051},
  volume       = {6},
  year         = {2012},
}