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Proteomic data analysis for differential profiling of the autoimmune diseases SLE, RA, SS, and ANCA-associated vasculitis

Ohlsson, Mattias LU orcid ; Hellmark, Thomas LU orcid ; Bengtsson, Anders A. LU ; Theander, Elke LU ; Turesson, Carl LU ; Klint, Cecilia LU ; Wingren, Christer LU and Ekstrand, Anna Isinger LU orcid (2021) In Journal of Proteome Research 20(2). p.1252-1260
Abstract

Early and correct diagnosis of inflammatory rheumatic diseases (IRD) poses a clinical challenge due to the multifaceted nature of symptoms, which also may change over time. The aim of this study was to perform protein expression profiling of four systemic IRDs, systemic lupus erythematosus (SLE), ANCA-associated systemic vasculitis (SV), rheumatoid arthritis (RA), and Sjögren's syndrome (SS), and healthy controls to identify candidate biomarker signatures for differential classification. A total of 316 serum samples collected from patients with SLE, RA, SS, or SV and from healthy controls were analyzed using 394-plex recombinant antibody microarrays. Differential protein expression profiling was examined using Wilcoxon signed rank test,... (More)

Early and correct diagnosis of inflammatory rheumatic diseases (IRD) poses a clinical challenge due to the multifaceted nature of symptoms, which also may change over time. The aim of this study was to perform protein expression profiling of four systemic IRDs, systemic lupus erythematosus (SLE), ANCA-associated systemic vasculitis (SV), rheumatoid arthritis (RA), and Sjögren's syndrome (SS), and healthy controls to identify candidate biomarker signatures for differential classification. A total of 316 serum samples collected from patients with SLE, RA, SS, or SV and from healthy controls were analyzed using 394-plex recombinant antibody microarrays. Differential protein expression profiling was examined using Wilcoxon signed rank test, and condensed biomarker panels were identified using advanced bioinformatics and state-of-the art classification algorithms to pinpoint signatures reflecting each disease (raw data set available at https:// figshare.com/s/3bd3848a28ef6e7ae9a9.). In this study, we were able to classify the included individual IRDs with high accuracy, as demonstrated by the ROC area under the curve (ROC AUC) values ranging between 0.96 and 0.80. In addition, the groups of IRDs could be separated from healthy controls at an ROC AUC value of 0.94. Disease-specific candidate biomarker signatures and general autoimmune signature were identified, including several deregulated analytes. This study supports the rationale of using multiplexed affinity-based technologies to reflect the biological complexity of autoimmune diseases. A multiplexed approach for decoding multifactorial complex diseases, such as autoimmune diseases, will play a significant role for future diagnostic purposes, essential to prevent severe organ- and tissue-related damage.

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author
; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Antibody microarray, Autoimmune diseases, Proteomics, Whole blood
in
Journal of Proteome Research
volume
20
issue
2
pages
1252 - 1260
publisher
The American Chemical Society (ACS)
external identifiers
  • pmid:33356304
  • scopus:85099212242
ISSN
1535-3893
DOI
10.1021/acs.jproteome.0c00657
language
English
LU publication?
yes
id
9e04d6d2-ebc8-45b2-851a-f48ada7bfe4c
date added to LUP
2021-01-25 10:33:50
date last changed
2024-08-08 11:05:42
@article{9e04d6d2-ebc8-45b2-851a-f48ada7bfe4c,
  abstract     = {{<p>Early and correct diagnosis of inflammatory rheumatic diseases (IRD) poses a clinical challenge due to the multifaceted nature of symptoms, which also may change over time. The aim of this study was to perform protein expression profiling of four systemic IRDs, systemic lupus erythematosus (SLE), ANCA-associated systemic vasculitis (SV), rheumatoid arthritis (RA), and Sjögren's syndrome (SS), and healthy controls to identify candidate biomarker signatures for differential classification. A total of 316 serum samples collected from patients with SLE, RA, SS, or SV and from healthy controls were analyzed using 394-plex recombinant antibody microarrays. Differential protein expression profiling was examined using Wilcoxon signed rank test, and condensed biomarker panels were identified using advanced bioinformatics and state-of-the art classification algorithms to pinpoint signatures reflecting each disease (raw data set available at https:// figshare.com/s/3bd3848a28ef6e7ae9a9.). In this study, we were able to classify the included individual IRDs with high accuracy, as demonstrated by the ROC area under the curve (ROC AUC) values ranging between 0.96 and 0.80. In addition, the groups of IRDs could be separated from healthy controls at an ROC AUC value of 0.94. Disease-specific candidate biomarker signatures and general autoimmune signature were identified, including several deregulated analytes. This study supports the rationale of using multiplexed affinity-based technologies to reflect the biological complexity of autoimmune diseases. A multiplexed approach for decoding multifactorial complex diseases, such as autoimmune diseases, will play a significant role for future diagnostic purposes, essential to prevent severe organ- and tissue-related damage.</p>}},
  author       = {{Ohlsson, Mattias and Hellmark, Thomas and Bengtsson, Anders A. and Theander, Elke and Turesson, Carl and Klint, Cecilia and Wingren, Christer and Ekstrand, Anna Isinger}},
  issn         = {{1535-3893}},
  keywords     = {{Antibody microarray; Autoimmune diseases; Proteomics; Whole blood}},
  language     = {{eng}},
  number       = {{2}},
  pages        = {{1252--1260}},
  publisher    = {{The American Chemical Society (ACS)}},
  series       = {{Journal of Proteome Research}},
  title        = {{Proteomic data analysis for differential profiling of the autoimmune diseases SLE, RA, SS, and ANCA-associated vasculitis}},
  url          = {{http://dx.doi.org/10.1021/acs.jproteome.0c00657}},
  doi          = {{10.1021/acs.jproteome.0c00657}},
  volume       = {{20}},
  year         = {{2021}},
}