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http://informahealthcare.com/bmk
ISSN: 1354-750X (print), 1366-5804 (electronic)
Biomarkers, Early Online: 1–11
!2014 Informa UK Ltd. DOI: 10.3109/1354750X.2014.920046
REVIEW ARTICLE
Systemic sclerosis biomarkers discovered using
mass-spectrometry-based proteomics: a systematic review
Paul Ba
˘la
˘nescu
1,2,3
#, Anca La
˘daru
4
, Eugenia Ba
˘la
˘nescu
1
, Cristian Ba
˘icu¸s
2,3,5
, and Gheorghe Andrei Dan
2,3,5
1
Clinical Immunology Department, Colentina Clinical Hospital, Bucharest, Romania,
2
Internal Medicine Department, University of Pharmacy and
Medicine ‘‘Carol Davila’’, Bucharest, Romania,
3
Clinical Research Unit, RECIF (Re
´seau d’ Epide
´miologie Clinique International Francophone),
Bucharest, Romania,
4
Institute for Mother and Child Protection ‘‘Alfred Rusescu’’, Bucharest, Romania, and
5
Department of Internal Medicine,
Colentina Clinical Hospital, Bucharest, Romania
Abstract
Context: Systemic sclerosis (SSc) is an autoimmune disease with incompletely known
physiopathology. There is a great challenge to predict its course and therapeutic response
using biomarkers.
Objective: To critically review proteomic biomarkers discovered from biological specimens from
systemic sclerosis patients using mass spectrometry technologies.
Methods: Medline and Embase databases were searched in February 2014.
Results: Out of the 199 records retrieved, a total of 20 records were included, identifying 116
candidate proteomic biomarkers.
Conclusion: Research in SSc proteomic biomarkers should focus on biomarker validation,
as there are valuable mass-spectrometry proteomics studies in the literature.
Keywords
Autoimmunity, biomarker validation,
proteomics, proteomic biomarkers,
systemic sclerosis
History
Received 22 April 2014
Accepted 28 April 2014
Published online 16 May 2014
Introduction
Systemic sclerosis (SSc) is an autoimmune disease that
associates three different physiopathological aspects: abnorm-
alities of the immune system (both cellular and humoral
immune response with synthesis of autoantibodies), skin
thickening due to fibrosis that is usually associated with
visceral fibrosis and fibroproliferative vasculopathy
(Bernatsky et al., 2009). A biomarker could be defined as a
factor that can be objectively measured and that serves as
surrogate marker of a physiologic or pathologic process.
Biomarkers have multiple applications in the medical field in
diagnosis, classification and disease extension, disease prog-
nosis or prediction of a specific therapeutic regimen
(Biomarkers Definitions Working Group, 2001). As the
complete pathogenesis of SSc is currently unknown, there is
a great challenge to predict the course of the disease and its
therapeutic response using biomarkers. At present, there is a
continuous effort to deliver biomarkers for early diagnosis,
disease activity assessment and SSc prognosis.
During recent years, proteomic biomarker discovery has
reached novel performances due to the development of mass
spectrometric technologies. These technologies allow detailed
assessment of protein composition of complex biological
samples like serum, saliva, bronchoalveolar lavage (Reid &
McLuckey, 2002). Biomarkers discovered within mass
spectrometry technologies could be valuable for SSc patients
and help in improving biomarker research. The aim of this
systematic review is to critically review proteomic biomarkers
discovered from biological fluids from SSc patients using
mass spectrometry technologies. In addition, the systematic
review will provide concise database for further validation
and exploration of such proteomic biomarkers, as most of
them have not yet been validated on independent cohorts.
This systematic review therefore provides possibilities for
future research in SSc proteomic biomarkers.
Methods
In order to complete the systematic review, two electronic
databases were searched. Article searching was performed in
February 2014 in Medline (1950–February 2014) and Embase
(1988–February 2014). The search strategy in Medline was as
follows: [(‘‘systemic sclerosis’’ (MeSH Terms) OR ‘‘sclero-
derma’’ (MeSH terms) OR ‘‘systemic sclerosis’’ (All fields)
OR ‘‘scleroderma’’ (All fields)) AND ‘‘mass spectrometry’’
(All fields)], [(‘‘systemic sclerosis’’ (MeSH Terms) OR
‘‘scleroderma’’ (MeSH Terms) OR ‘‘systemic sclerosis’’ (All
fields) OR ‘‘scleroderma’’ (All fields)) AND ‘‘2D electro-
phoresis’’ (All fields)], [(‘‘systemic sclerosis’’ (MeSH Terms)
OR ‘‘scleroderma’’ (MeSH Terms) OR ‘‘systemic sclerosis’’
(All fields) OR ‘‘scleroderma’’ (All fields)) AND (‘‘prote-
omics’’ (MeSH Terms) OR ‘‘proteomics’’ (All fields) OR
‘‘proteome’’ (MeSH Terms) OR ‘‘proteome’’ (All fields)].
Embase searching strategy was similar with the one in
Medline, as follows: [(‘‘systemic sclerosis’’ OR ‘‘sclero-
derma’’) AND ‘‘mass spectrometry’’], [(‘‘systemic sclerosis’’
#Paul Ba
˘la
˘nescu is responsible for statistical design/analysis. E-mail:
plbalanescu@gmail.com
Address for correspondence: Paul Ba
˘la
˘nescu, Clinical Immunology
Department, Colentina Clinical Hospital, 19-21 Stefan cel Mare Street,
Bucharest, Romania. E-mail: plbalanescu@gmail.com
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OR ‘‘scleroderma’’) AND ‘‘2D electrophoresis’’], [(‘‘sys-
temic sclerosis’’ OR ‘‘scleroderma’’) AND (‘‘proteomics’’
OR ‘‘proteome’’)]. Articles retrieved were imported in
EndNote version X7.0.1 (Thomson Reuters, New York,
NY). Eligibility criteria analysis of articles retrieved by the
previous research strategy was performed by two independent
reviewers (PB and AL) who reviewed the abstracts. Any
discrepancies were solved with discussions between the two
reviewers. Letters to the editor, review articles or case reports
were excluded from the analysis. Studies referring to animal
models were also excluded, so were studies that referred to
other systemic autoimmune diseases (that were retrieved
in the electronic search due to the word ‘‘systemic’’,
especially systemic lupus erythematosus, multiple sclerosis,
polymiositis). Only articles in English were considered. From
the remaining articles, only those that used mass spectrometry
technologies to compare biological fluids from SSc patients
compared to a control group were considered. Each included
study had data extracted within a database that consisted of:
first author, year of publication, number of SSc patients,
number of controls, type of controls (healthy or other type of
control group), type of SSc (limited, diffuse, SSc that
associated pulmonary fibrosis, pulmonary hypertension),
type of biological fluid analyzed, mass spectrometry
technology used, proteomic biomarkers identified and fold
change versus controls (if the details were given in the paper),
validation of biomarkers with immunological methods if it
was considered in the paper.
Results
After duplicate removal, 199 records were analyzed and
finally a total of 20 records were included in the systematic
review. Out of these, 16 full text research articles were
summarized (Aden et al., 2008; Baldini et al., 2011;
Bargagli et al., 2008; Bogatkevich et al., 2008; Chiang
& Postlethwaite, 2006; De Santis et al., 2011; Del Galdo
et al., 2010; Fietta et al., 2006; Giusti et al., 2007;
Landi et al., 2013; Larsen et al., 2006; Rottoli et al., 2005;
Scambi et al., 2010; Shirahama et al., 2010; Van Bon et al.,
2014; Xiang et al., 2007) and information found in the
abstracts were included for the three relevant conference
proceedings found (Coral-Alvarado et al., 2012; Radstake
et al., 2010; Van Bon et al., 2010) and for the research article
whose full-text could not have been retrieved (Guerranti
et al., 2010). The entire search process with its results and
also the exclusion criteria of the papers are presented in
Figure 1. Table 1 refers to studies included in the systematic
Figure 1. The flow chart for the search results
from the databases with inclusion and exclu-
sion criteria of the reports.
2P. B a
˘la
˘nescu et al. Biomarkers, Early Online: 1–11
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Table 1. Main findings of studies included in the systematic review.
Reference Biological sample Sample size (SSc/control) Proteomic technique used Proteomic biomarker discovered Validation
Aden et al.
(2008)
Cutaneous punch biopsy from
fibrotic lesion
12 diffuse SSc /12 sex and age-
matched healthy controls
2DE, MALDI-TOF/MS Cytokeratin 1, cytokeratin 14, cytokeratin 5, caspase 14
precursor, galectin 7,Heat shock protein 27, 40S
ribosomal protein SA, tropomyosin, vimentin, calre-
ticulin precursor, cyclophilin A, serum amyloid P-
component precursor, alpha 1 anti-trypsin precursor,
cathepsin D precursor, ATP synthase b chain,
fructose biphosphate aldolase V, carbonyl reductase
I, peroxiredoxin 1, collagen a-2 precursor (VI),
collagen a-3 precursor (VI), annexin A2, carbonic
anhydrase
IHC
Baldini et al.
(2011)
Unstimulated saliva 7 SSc patients with secondary
Sjogren syndrome/40 healthy
controls
2DE, MALDI-TOF/MS Cystatin C, b-2 microglobulin, Glyceraldehyde 3-
phosphate dehydrogenase
ELISA
Bargagli et al.
(2008)
Bronchoalveolar lavage fluid 11 pulmonary fibrosis SSc
patients/11 sex and age matched
controls
2DE, MALDI TOF/MS Calgranulin B N/A
Bogatkevich
et al. (2008)
Protein extract from fibroblasts
culture from pulmonary biopsy
3 SSc patients with pulmonary
fibrosis/3 age and sex healthy
controls
2DE, MALDI TOF/TOF,
LC-ESI-MS/MS
Pro-acollagen, caldesmon, prolyl 4-hydroxylase b
subunit, IQGAP1, ezrin, moesin, BiP glucose
regulated protein, ER-60 protease, heterogeneous
ribonucleoprotein U (HNRPU), valosin-containing
protein, stress-induced phosphoprotein-A
WB
Chiang &
Postlethwaite
(2006)
Platelet lysate from rich platelet
plasma
6 SSc patients/6 healthy controls 2DE, MALDI-TOF/MS Phosphatidylinositol-3 kinase (PI 3-K) WB
Coral-Alvarado
et al. (2012)
Fibroblast culture supernatant
from skin
11 SSc patients/11 healthy controls 2DE, MALDI-TOF/MS Haptoglobin N/A
De Santis et al.
(2011)
Bronchoalveolar lavage fluid 46 SSc patients with pulmonary
fibrosis/15 healthy controls
RP-HPLC-ESI-MS Thymosin b4ELISA
Del Galdo et al.
(2010)
Fibroblast culture from skin
supernatant
6 diffuse SSc/6 sex and age
mathced healthy controls
2DE DIGE, LC-MS/MS Reticulocalbin 3, reticulocalbin 1, calumenin, osteo-
nectin, a2 chain type collagen, tropomyosin 4,
enolase 1, calreticulin precursor, aactin, pigment
epithelium derived factor
IF and WB
Fietta et al.
(2006)
Bronchoalveolar lavage fluid 9 SSc patients with pulmonary
fibrosis/6 SSc patients without
pulmonary fibrosis
2DE, MALDI, LC-MS/MS a1 acid glycoprotein, Cu-Zn superoxide dismutase,
albumin, calgranulin B, haptoglobin achain, gluta-
thione S transferase P, cystatin SN, cytohesin 2,
calumenin, mithocondrial DNA topoisomerase 1
N/A
Giusti et al.
(2007)
Unstimulated saliva 15 diffuse SSc patients/15 sex and
age matched healthy controls
2DE, MALDI-TOF/MS Keratin 6L, calgranulin A, calgranulin B, psoriasin, b-2
microglobulin, cystatin B, cyclophilin A, glyceral-
dehyde 3-phosphate dehydrogenase, triose-phos-
phate-isomerase, actin-related protein 2/3 complex
subunit 2
N/A
Guerranti et al.
(2010)
Serum 23 SSc patients/21 healthy controls 2DE Haptoglobin 2-2 isoform WB
Landi et al.
(2013)
Bronchoalveolar lavage fluid 7 SSc patients with pulmonary
fibrosis/10 non smoking con-
trols and 8 smoking controls
2DE, MALDI-TOF/MS,
LC-MS/MS
a1-Antitrypsin, pulmonary surfactant associated protein
A2, a-2-HS-glycoprotein, complement factor H,
protein S100A6, apolipoprotein AI, transthyretin,
Zinc-alpha-2-glycoprotein, Retinol binding protein,
14-3-3 protein epsilon, calcyphosin, ceruloplasmin,
WB
(continued )
DOI: 10.3109/1354750X.2014.920046 SSc biomarkers 3
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Table 1. Continued
Reference Biological sample Sample size (SSc/control) Proteomic technique used Proteomic biomarker discovered Validation
Macrophage mannose receptor 1, b2 microglobulin,
lysozyme C, serpin B3, Immunoglobulin J chain,
complement C3 achain, angiotensinogen, haptoglo-
bin, L-FABP, selenium binding protein
Larsen et al.
(2006)
Myofibroblast lysate from cultures
originating from bronchoalveo-
lar lavage fluid
10 SSc patients with lung fibrosis
and alveolitis/5 mild asthma
patients
2DE, MALDI-TOF/MS Vimentin, actin-related protein 3, actin related protein
2/3 (p16-ARC), tropomyosin, Ran specific GTP-ase
activating protein (RanBP1), stathmin, glutathione S
transferase P, ubiquitin carboxyl-terminal hydrolase
isoenzyme, thioredoxin-dependent peroxidase
reductase precursor, disulfide isomerase ER-60
(Erp60), 6-phosphogluconolactonase, apolipoprotein
AI precursor, keratin 10
N/A
Radstake et al.
(2010)
Plasmacytoid dendritic cell culture
supernatant from peripheral
blood
244 SSc/ 129 healthy controls SELDI-TOF/MS CXCL-4 ELISA
Rottoli et al.
(2005)
Bronchoalveolar lavage fluid 10 pulmonary fibrosis SSc
patients/11 patients with idio-
pathic pulmonary fibrosis and
11 patients with pulmonary
sarcoidosis
2DE a1-b-glycoprotein, complement C3 b, complement
factor I, a1-Antitrypsin, haptoglobin b, serum retinol
binding protein (SRBP), transthyretin, cyclophilin A,
calgranulin A, calgranulin B, complement C3,
translationally controlled tumor protein (p23),
migration inhibition factor (MIF), galectin 1, ubi-
quitin, prothrombin, thioredoxin peroxidase 2, L fatty
acid binding protein (L-FABP), peroxisomal anti-
oxidant enzyme (AOPP)
N/A
Scambi et al.
(2010)
Serum 11 limited SSc, 15 diffuse SSc
patients/15 sex and age matched
healthy controls
2DE, MALDI MS, LC MS/
MS
Complement factor H, transthyretin, Bence Jones
protein, apolipoprotein AI precursor, apolipoprotein
AIV precursor, amyloid serum-related protein,
platelet basic protein precursors, CD5 antigen like
ELISA
Shirahama et al.
(2010)
Bronchoalveolar lavage fluid 5 SSc patients with pulmonary
fibrosis/4 SSc patients without
pulmonary fibrosis
2DE, MALDI-TOF/MS a1-Antitrypsin, pulmonary surfactant protein A, a2-
macroglobulin, a2 heat shock protein, glutathione S
transferase P
N/A
Van Bon et al.
(2010)
Serum 19 diffuse SSc patients and 21
limited SSc patients/20 healthy
controls
2DE, MALDI-TOF/MS Calgranulin A N/A
Van Bon et al.
(2014)
Plasmacytoid dendritic cell culture
supernatant from peripheral
blood
1 limited SSc, 2 early diffuse SSc,
1 late diffuse SSc/2 healthy
controls
SELDI-TOF/MS CXCL-4, CTAP-III, calgranulin A (S100A8), calgra-
nulin B (S100A9), calprotectin (S100A8/A9),
lysozyme
ELISA
Xiang et al.
(2007)
Serum 40 SSc/24 age and sex controls MB-HIC, MALDI TOF MS Degraded derivative C3f peptides, C4, clusterin, ITIH-4
(inter-atrypsin inhibitor heavy chain H4 precursor),
albumin, apolipoprotein AIV precursor
N/A
2DE: two-dimension electrophoresis; 2DE-DIGE: two-dimension electrophoresis fluorescence difference gel electrophoresis; MALDI: Matrix-assisted laser desorption/ionization; TOF: Time of flight; SELDI-
TOF/MS: surface-enhanced laser desorption/ionization-time of flight mass spectrometry; RP-HPLC-ESI-MS: Reverse phase-high performance liquid chromatography-Electrospray-Mass spectrometry; LC-MS/
MS: Liquid chromatography-mass spectrometry; LC-ESI-MS/MS: Liquid chromatography-electrospray ioniozation-mass spectrometry; MB-HIC: Magnetic beads-based hydrophobic interaction; IHC:
Immunohistochemistry; ELISA: Enzyme-linked immunosorbent assay; WB: western blot; IF: Immunofluorescence; N/A-not applicable; without validation. Only highlighted (bold) proteins in table were
validated by the authors.
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review and summarizes the main findings of each study
(biological sample, controls, proteomic technique used,
biomarkers discovered, and whether the biomarkers were
validated within the study and validation method). In
addition, the 16 full text articles retrieved were evaluated
according to the recommendations suggested for biomarker
identification in clinical proteomics (Mischak et al., 2010).
Results are found in Table 2 and all studies fulfilled at
least four recommendations out of eight. The last three
recommendations were among the most unfulfilled criteria
(validation of results, limitation acknowledgement of the
study and author contribution statement).
Biological samples
The systematic review included studies that focused upon a
variety of biological samples from SSc patients. Most
biological samples in the studies included were bronchoal-
veolar lavage fluid (BALF) (six out of 20 studies, Bargagli
et al., 2008; De Santis et al., 2011; Fietta et al., 2006; Landi
et al., 2013; Rottoli et al., 2005; Shirahama et al., 2010) as
biomarkers associated with pulmonary manifestations of SSc
are needed. Serum was the second most analyzed biological
fluid (four out of 19 studies, Guerranti et al., 2010; Scambi
et al., 2010; Van Bon et al., 2010; Xiang et al., 2007), as it is
a biological sample that is easy to obtain by venipuncture,
easy to process and analyze. Two studies analyzed the
salivary proteome, using whole unstimulated saliva (Baldini
et al., 2011; Giusti et al., 2007) as SSc could be associated
with secondary Sjogren syndrome and saliva is a biological
sample relatively easy to obtain from patients. One study
(Aden et al., 2008) analyzed the protein extract from
cutaneous biopsies and one study (Chiang & Postlethwaite,
2006) analyzed rich platelet plasma. Finally, six studies
analyzed proteome of cell cultures derived from biological
samples (four studies referred to fibroblast proteome or
secretome) (Bogatkevich et al., 2008; Coral-Alvarado et al.,
2012; Del Galdo et al., 2010; Larsen et al., 2006) as the
fibroblast is one of the main cell implicated in SSc
pathogenesis and two studies (Radstake et al., 2010; Van
Bon et al., 2014) referred to secretome of plasmacytoid
dendritic cells of SSc patients) (Table 1). Such ‘‘in vitro’’
studies were not excluded from the analysis because prote-
omic biomarker identification within cell culture-derived
secretome or proteome could be accelerated with these
studies. Proteins that are synthesized and secreted by
fibroblasts or plasmacytoid dendritic cells from patients
with SSc and that can be detected from cell cultures by mass-
spectrometry based proteomics could also be detected ‘‘in
vivo’’ (in samples from SSc patients) and potentially proved
to be valuable biomarkers. Therefore, this systematic review
considered these ‘‘in vitro’’ studies as well for potential
biomarker candidate identification that can be further
evaluated in SSc patients.
Table 2. Suggested requirements for scientific reporting of proteomic biomarker data stated by Mischak et al. (2010) applied for the 16 full-text
articles retrieved in the systematic review.
Requirement for
scientific reporting Papers that fulfill the requirements
Justification and description of
clinical question, outcomes and
selection of subjects
All papers: Aden et al. (2008); Baldini et al. (2011); Bargagli et al. (2008); Bogatkevich et al. (2008);
Chiang & Postlethwaite (2006); De Santis et al. (2011); Del Galdo et al. (2010); Fietta et al. (2006);
Giusti et al. (2007); Landi et al. (2013); Larsen et al. (2006); Rottoli et al. (2005); Scambi et al.
(2010); Shirahama et al. (2010); Van Bon et al. (2014); Xiang et al. (2007)
Assessed subjects description All papers: Aden et al. (2008); Baldini et al. (2011); Bargagli et al. (2008); Bogatkevich et al. (2008);
Chiang & Postlethwaite (2006); De Santis et al. (2011); Del Galdo et al. (2010); Fietta et al. (2006);
Giusti et al. (2007); Landi et al. (2013); Larsen et al. (2006); Rottoli et al. (2005); Scambi et al.
(2010); Shirahama et al. (2010); Van Bon et al. (2014); Xiang et al. (2007)
Sampling description All papers: Aden et al. (2008); Baldini et al. (2011); Bargagli et al. (2008); Bogatkevich et al. (2008);
Chiang & Postlethwaite (2006); De Santis et al. (2011); Del Galdo et al. (2010); Fietta et al. (2006);
Giusti et al. (2007); Landi et al. (2013); Larsen et al. (2006); Rottoli et al. (2005); Scambi et al.
(2010); Shirahama et al. (2010); Van Bon et al. (2014); Xiang et al. (2007)
Experimental methodology
description
Aden et al. (2008); Baldini et al. (2011); Bogatkevich et al. (2008); Chiang & Postlethwaite (2006); De
Santis et al. (2011); Del Galdo et al. (2010); Fietta et al. (2006); Giusti et al. (2007); Landi et al.
(2013); Larsen et al. (2006); Rottoli et al. (2005); Scambi et al. (2010); Shirahama et al. (2010); Van
Bon et al. (2014); Xiang et al. (2007)
Statistical evaluation description Aden et al. (2008); Baldini et al. (2011); Bogatkevich et al. (2008); De Santis et al. (2011); Del Galdo
et al. (2010); Fietta et al. (2006); Giusti et al. (2007); Landi et al. (2013); Larsen et al. (2006);
Rottoli et al. (2005); Scambi et al. (2010); Shirahama et al. (2010); Van Bon et al. (2014); Xiang
et al. (2007)
Result validation Aden et al. (2008); Baldini et al. (2011); Bogatkevich et al. (2008); Chiang & Postlethwaite (2006); De
Santis et al. (2011); Del Galdo et al. (2010); Landi et al. (2013); Scambi et al. (2010); Van Bon
et al. (2014)
Acknowledge limitations Baldini et al. (2011); Bargagli et al. (2008); Del Galdo et al. (2010); Giusti et al. (2007); Shirahama
et al. (2010)
Contribution of each author clearly
stated
Baldini et al. (2011); De Santis et al. (2011); Larsen et al. (2006); Scambi et al. (2010); Xiang et al.
(2007)
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Proteomic techniques
Various biological samples were analyzed in studies included
in the systematic review and this was challenging because
these samples are rich in proteins of wide range concentra-
tion. Three studies used protein extraction methods, as
proteins from the biological sample could not have been
otherwise separated (Aden et al., 2008; Bogatkevich et al.,
2008; Larsen et al., 2006). One study used sonication
methods for lysing cells and obtaining the protein samples
(Chiang & Postlethwaite, 2006). In order to identify prote-
omic biomarkers from the samples, they were separated into
fractions or spots. Most studies (15 out of 20) used two-
dimension electrophoresis (2DE) as initial protein profiling of
the samples, demonstrating that 2DE is a popular separation
proteomic technique. Only one study (Del Galdo et al., 2010)
used two-dimension electrophoresis fluorescence difference
gel electrophoresis (2D-DIGE) for protein separations.
2D-DIGE compensates some of the drawbacks of 2DE in
terms of reproducibility, allowing internal standard running at
each separation and researchers to run two samples simul-
taneously. One study (Xiang et al., 2007) used magnetic
beads-based hydrophobic interaction chromatography,
which is a technique that allows reduction in time and costs
of separation thus simplifying the process. In one study
(De Santis et al., 2011), sample matrix (Protein Chip) was
used for protein separation and purification.
Fractions or spots that were up- or downregulated versus
controls were identified as potential candidate biomarkers by
comparing separated samples from SSc patients with controls,
using appropriate statistical methods. Each spot or fraction
was analyzed by mass-spectrometry means having their mass
identified. Each mass data was compared with existing data
from protein databases for identification. Studies included
in the systematic review used the following proteomic
techniques: 2DE, 2DE-DIGE, matrix-assisted laser desorp-
tion/ionization-time of flight mass spectrometry (MALDI-
TOF/MS), surface-enhanced laser desorption/ionization-time
of flight mass spectrometry (SELDI-TOF/MS), liquid chro-
matography-mass spectrometry (LC-MS/MS), reverse phase-
high performance-liquid chromatography-electrospray-mass
spectrometry (RP-HPLC-ESI-MS), liquid chromatography-
electrospray ioniozation-mass spectrometry (LC-ESI-MS/
MS) (Table 1). Most of the studies associated 2DE with
mass spectrometry methods and in some studies where spots
could not have been identified using MALDI-TOF further
used LC-MS/MS for identification. Usually LC-MS/MS
methods are used for the identification of low molecular
weight proteins.
Selection of control group
Selection of controls is of critical importance for accurate
biomarker identification. As expected for biomarker identifi-
cation, most studies (16 out of 20) compared biological
samples from SSc patients with healthy controls (seven
studies used sex and age-matched healthy controls, while nine
did not mention whether healthy controls were matched with
SSc patients). However, in four studies out of 20, the
proteomic profile of SSc patients was compared to non-
healthy controls. All four studies aimed to determine
biomarkers related to SSc pulmonary fibrosis. One study
compared proteomic profiles of pulmonary SSc patients with
the ones of related fibrotic pulmonary diseases-sarcoidosis
and idiopathic pulmonary fibrosis (Rottoli et al., 2005) in
order to determine biomarkers that are associated specifically
to pulmonary fibrosis related to SSc. Another study used mild
asthmatic controls (Larsen et al., 2006), while two studies
aimed to determine biomarkers that could be associated
with pulmonary SSc within SSc, comparing proteomic
profiles of pulmonary SSc patients with SSc patients that
had no pulmonary manifestations (Fietta et al., 2006;
Shirahama et al., 2010).
Proteomic biomarkers identified
A total of 116 candidate proteomic biomarkers were
identified. Most of them (91) were upregulated from bio-
logical samples of SSc patients. Twenty proteomic biomarkers
were downregulated and five proteomic biomarkers were
upregulated in some reports or downregulated in others
(glyceraldehyde 3-phosphate dehydrogenase, b2 microglobu-
lin, glutathione S transferase P, disulfide isomerase ER-60,
calgranulin B) (Supplementary Table 1). As expected, most
biomarkers were found in BALF (48 biomarkers), followed by
biomarkers found in the proteome analysis of cell cultures
derived from biological samples (40 biomarkers) and bio-
markers found in cutaneous biopsies of SSc patients (22
biomarkers). In serum 15 biomarkers were identified and in
saliva 10 biomarkers were detected. Finally, one biomarker
was identified in platelet-rich plasma (Table 3).
Seventeen proteomic biomarkers were found in multiple
biological fluids (tropomyosin, vimentin, calreticulin precur-
sor, cyclophilin A, alpha 1 anti-trypsin, b2-microglobulin,
calgranulin A, calgranulin B, haptoglobin, transthyretin,
albumin, gluthatione-S-transferase P, calumenin, complement
factor H, apolipoprotein AI precursor, lysozyme C, disulfide
isomerase Er-60) (Supplementary Table 1).
In order to systemize candidate biomarkers identified
within the systematic review, the 116 biomarkers found were
grouped in 14 categories as follows: biomarkers involved in
epidermis development, cell adhesion molecules, chaperons,
cell contractility and cytoskeleton proteins, acute phase
reactants, biomarkers involved in protein metabolism, bio-
markers involved in cellular energetic metabolism, oxidative
stress, extracellular matrix components, cytokines, chemo-
kines and immunomodulators, complement system, bio-
markers involved in lipid metabolism, intracellular signal
transduction biomarkers and others (Supplementary Table 2).
Validation of biomarkers
For proteomic biomarker confirmation they should also be
identified using immune-based detection assays, preferably
upon samples of an independent confirmation cohort. Eleven
studies confirmed some of the candidate biomarkers identi-
fied. From the total of 116 candidate biomarkers discovered,
only 11 biomarkers were confirmed (cytokeratin 14, galectin
7, heat shock protein 27, b2 microglobulin, thymosin b4,
protein S100A6, 14-3-3 protein epsilon, reticulocalbin 1,
complement factor H, CXCL4, IQGAP1). The studies used
Western Blot (WB) and ELISA in the same proportion for
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confirmation (five studies each). Only one study confirmed its
biomarkers with immunohistochemistry (IHC) (Aden et al.,
2008) and one study combined WB and immunofluorescence
(Del Galdo et al., 2010) (Table 1).
Discussion
This systematic review had the aim to review proteomic
biomarkers discovered using mass spectrometry proteomics in
SSc patients. Although the approach is still unconventional,
systemizing potential biomarkers found using mass spectrom-
etry methods has already been used in the literature for
diseases like polycystic ovary syndrome or hepatocellular
carcinoma (Atiomo et al., 2009; Liu et al., 2011). Such
systematic reviews are helpful for biomarker research as they
summarize most candidate biomarkers to be further evaluated.
It is known that mass spectrometry proteomics report still
unconfirmed biomarkers that should be further considered.
This systematic review identified potential SSc biomarkers
detected using mass spectrometry proteomics and 2 DE.
Some of them are proteins located within the cells and are not
secreted in the biological fluids, while others have increased
expression extracellulary. The latter are more important
for diagnostic means as they can be easily detected using
routine laboratory methods. Totally, 14 categories of bio-
markers were found from SSc biological specimens and most
biomarkers were upregulated (Supplementary Table 2).
Biomarker up-regulation make them easily identifiable from
biological specimens of SSc patients, so they can be
efficiently incorporated in future proteomic diagnostic tools.
It is obvious that biomarker research process in SSc
patients focused mainly upon fibrosis biomarker research, as
it is a hallmark of SSc. For interest, pulmonary fibrosis and
cutaneous fibrosis biomarkers are envisaged. Modified
Rodnan skin score (mRSS) is used for skin fibrosis measure-
ment in both clinical practice and clinical trials. The main
disadvantage of mRSS is that its assessment lacks total
objectivity, as it is performed by the physician. Therefore,
biomarkers are needed in order to objectively assess skin
fibrosis. Del Galdo et al. (2010) suggested that these
proteomic biomarkers should be looked after in the dermal
fibroblast secretome of SSc patients. They identified a group
of candidate biomarkers that could be associated with skin
fibrosis (CREC family members) and identified calumenin,
reticulocalbin 1 and 3 upregulated in the dermal fibroblast
secretome. They confirmed reticulocalbin 1 in both SSc skin
and serum using WB. Interestingly, calumenin was also
upregulated in BALF of pulmonary SSc fibrosis patients in
another study (Fietta et al., 2006). CREC family members are
chaperons located in the endoplasmic reticulum. Some family
members are also secreted calumenin and therefore they are
potential proteomic biomarkers to be identified from bio-
logical fluids of SSc patients. CREC family members are
Table 3. Candidate proteomic biomarkers identified and ordered by the biological specimens from SSc patients.
Biological sample Biomarkers identified
BALF a1 acid glycoprotein, a1-Antitrypsin, a1-b-glycoprotein, a2 heat shock protein, a-2-HS-glycoprotein,
a2-macroglobulin, 14-3-3 protein epsilon, albumin, angiotensinogen, apolipoprotein AI, calcyphosin,
calgranulin A, calgranulin B, calumenin, ceruloplasmin, complement C3, complement C3 b, complement
C3 achain, complement factor H, complement factor I, Cu-Zn superoxide dismutase, cyclophilin A, cystatin
SN, cytohesin 2, galectin 1, glutathione S transferase P, haptoglobin, immunoglobulin J chain, L fatty acid
binding protein (L-FABP), lysozyme C, macrophage mannose receptor 1, migration inhibition factor (MIF),
mithocondrial DNA topoisomerase 1, peroxisomal antioxidant enzyme (AOPP), protein S100A6, pro-
thrombin, pulmonary surfactant associated protein A2, pulmonary surfactant protein A, selenium binding
protein, serpin B3, serum retinol binding protein (SRBP), thioredoxin peroxidase 2, thymosin b4,
translationally controlled tumour protein (p23), transthyretin, ubiquitin, zinc- a-2-glycoprotein
Proteome analysis of cell cultures
derived from biological samples
aactin, a2 chain type collagen, 6-phosphogluconolactonase, actin related protein 2/3 (p16-ARC), actin-related
protein 3, apolipoprotein AI precursor, BiP glucose regulated protein, caldesmon, calgranulin A (S100A8),
calgranulin B (S100A9), calprotectin (S100A8/A9), calreticulin precursor, calumenin, CTAP-III, CXCL-4,
disulfide isomerase ER-60 (Erp60), enolase 1, ER-60 protease, ezrin, glutathione S transferase P, haptoglobin,
heterogeneous ribonucleoprotein U (HNRPU), IQGAP1, keratin 10, lysozyme, moesin, osteonectin, pigment
epithelium derived factor, prolyl 4-hydroxylase bsubunit, pro-acollagen, Ran specific GTP-ase activating
protein (RanBP1), reticulocalbin 1, reticulocalbin 3, stathmin, stress-induced phosphoprotein-A, thioredoxin-
dependent peroxidase reductase precursor, tropomyosin, ubiquitin, carboxyl-terminal hydrolase isoenzyme,
valosin-containing protein, vimentin
Cutaneous biopsy a1 anti-trypisin precursor, 40S ribosomal protein SA, annexin A2, ATP synthase b chain, calreticulin precursor,
carbonic anhydrase, carbonyl reductase I, caspase 14 precursor, cathepsin D precursor, collagen a-2 precursor
(VI), collagen a-3 precursor (VI), cyclophilin A, cytokeratin 1, cytokeratin 14, cytokeratin 5, fructose
biphosphate, aldolase V, galectin 7, heat shock protein 27, peroxiredoxin 1, serum amyloid P-component
precursor, tropomyosin, vimentin
Serum Albumin, amyloid serum related protein, apolipoprotein AI precursor, apolipoprotein AIV precursor, Bence
Jones protein, C4, calgranulin A, CD5 antigen like, clusterin, complement factor H, degraded derivative C3f
peptides, haptoglobin 2-2 isoform, ITIH-4 (inter-atrypsin inhibitor heavy chain H4 precursor), platelet basic
protein precursors, transthyretin
Saliva b2 microglobulin, actin-related protein 2/3 complex subunit 2, calgranulin A, calgranulin B, cyclophilin A,
cystatin B, cystatin C, glyceraldehyde 3-phosphate dehydrogenase, keratin 6L, psoriasin, triose-phosphate-
isomerase
Rich platelet plasma Phosphatidylinositol-3 kinase (PI 3-K)
BALF: bronchoalveolar lavage fluid.
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mainly implicated in the secretion process and it seems that
they have an autocrine or paracrine effect upon fibroblasts on
organization of the cytoskeleton (Ostergaard et al., 2006).
Therefore, they seem to be promising biomarkers to be
associated with fibrosis in SSc (both pulmonary or cutane-
ous). Nevertheless, their serum expression still needs to be
correlated with both pulmonary fibrosis and mRSS in future
studies upon independent cohorts using quantitative assays
in order to establish their role as fibrosis biomarker and
whether they could differentiate fibrotic processes from either
SSc lung or skin.
Apart from CREC family members, there were also other
chaperons identified as potential biomarkers associated to
SSc. Extracellular chaperons seem to be more important in
means of identification within biological fluids of patients.
Clusterin was upregulated in SSc patients serum and also
seems to be a potential biomarker of SSc. Clusterin is
implicated in a large number of processes, such as anti-
inflammatory and anti-apoptotic functions and have been
recently identified as potential biomarker for various cancers
(Koltai, 2014). It has also been identified using ELISA as
potential protective factor for digital ulcers and pulmonary
hypertension in SSc patients (Yanaba et al., 2012). From all
these, one can consider that secreted fibroblastic chaperons
can be promising biomarkers for SSc fibrosis assessment and
they should be further analyzed.
As for pulmonary fibrosis assessment, the systematic
review revealed 48 candidate proteomic biomarkers from
patients’ BALF with pulmonary SSc fibrosis that could be
useful for differentiating pulmonary manifestations of SSc
from other pulmonary fibrotic conditions. These biomarkers
could also predict for pulmonary SSc involvement. However,
because bronchoscopy remains an invasive procedure, bio-
markers expressed in both BALF and serum would be more
helpful, as it would ease patients’ monitoring. The systematic
review pinpointed six biomarkers expressed in both BALF
and serum: b2 microglobulin, calgranulin A, transthyretin,
haptoglobin, apolipoprotein AI and albumin. However, some
of them could be present in the BALF as the result of the
exudative response due to pulmonary inflammation. Studies
that aim to confirm these biomarkers should also determine
the relationship between their serum and BALF expression.
As expected, because SSc is associated with an inflam-
matory response, all acute phase reactants were upregulated
from biological specimens of SSc patients. Unfortunately,
acute phase reactants are not specific and therefore they are
not useful for diagnosis. However, they could be useful as
prognosis biomarkers or for therapy tailoring. Particulary,
haptoglobin 2-2 ispotype was identified and seem to be a
potential biomarker for SSc patients, as it is known to be
associated with an increase in fibrotic response and switch
from Th1 to Th2 type immune (Sadrzadeh & Bozorgmehr,
2004). Inflammatory signals associated with Th2 immune
response induce fibrosis.
Complement system proteins and some cytokines and
chemokines were also identified as candidate biomarkers for
SSc. Being an autoimmune disease, SSc is associated with
immune response deregulation and autoantibody synthesis.
Studies include potential role of CD5-antigen like, platelet
basic protein precursors and platelet CXC chemokines
(CXCL4, CTAP-III) as valuable biomarkers. CXC platelet
chemokines are implicated mainly in angiogenesis, CXCL4 is
associated with an inhibition of angiogenesis and CTAP-III as
angiogenesis promoter (Pilatova et al., 2013). Two study
groups (Radstake et al., 2010; Van Bon et al., 2014) have
identified and already confirmed upon large cohorts of
CXCL4 as biomarker for SSc, mainly for disease progression
prediction and hallmark for very early diffuse SSc. In
addition, CXCL4 strongly correlated with pulmonary involve-
ment and skin fibrosis in diffuse SSc patients. As SSc is
associated with abnormal inflammatory angiogenesis and a
disbalance between proangiogenic and antiangiogenic mol-
ecules, platelet chemokines balance (CXCL4/CTAP-III)
seems to be attractive as SSc biomarker. However, as
CXCL4 levels alone were considered in previous studies,
CTAP-III expression in SSc patients still needs to be
investigated in association with CXCL4.
Some of the complement system molecules were also
differently expressed from controls. Being an autoimmune
disease with autoantibody synthesis it is expected that
complement levels to be decreased secondary to their
consumption. Studies included in the systematic review
revealed a decrease of C3 and C3a with up-regulation of
C3b and degraded derivative C3f peptides (generated from
C3b inactivation). On the other hand, complement factor I and
H are implicated in inhibition of complement activation.
As complement factor I was found to be downregulated,
complement factor H was upregulated but Scambi and
collaborators noticed that in SSc patients factor H was
dysfunctional ‘‘in vitro’’ mainly due to its inability of
attachment to surface membranes and therefore did not
protect anymore cellular membranes from complement lysis
(Scambi et al., 2010). As hypocomplementemia is not
particulary associated with SSc, it was previously reported
in the literature (Hudson et al., 2007). In SSc patients,
complement activation is not as important as it is in systemic
lupus erythematosus (SLE) and complement levels are not
severely dropped as they are in SLE. In this light, complement
inhibitors and complement degraded peptides could play a
more important role as SSc biomarkers and should be further
investigated and correlated with complement levels.
In addition to inflammatory markers, most proteins that are
associated with oxidative stress were found to be upregulated
from biological samples of SSc patients. As chronic inflam-
mation is usually accompanied with increase in the oxidative
stress, up- or down-regulation of these candidate proteomic
biomarkers could be considered more as an epiphenomenon.
Although a variety of biological specimens were analyzed,
some biomarkers were consistent (Supplementary Table 1).
These biomarkers seem to be more promising due to their
reproducibility in different study groups; however, they could
lack correlation or predictive capacities for a particular
clinical SSc aspect. A small number of biomarkers were
upregulated in some studies and downregulated in others.
This happened mainly because of the variation of the control
groups. Most of the studies used healthy controls, but there
were two studies (Larsen et al., 2006; Rottoli et al. 2005)
that used as control groups, other pulmonary fibrotic condi-
tions and mild asthmatics. In respect to these groups the
biomarkers have been differently expressed.
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Some of the biomarkers that were identified by mass
spectrometry proteomics were also identified by other
research groups with different proteomic immune-based
techniques (like WB or ELISA), such as the case of clusterin
as protective factor for pulmonary hypertension, psoriasin
(S100A7) as predictor of pulmonary fibrosis (Baldini et al.,
2008), calgranulins A and B and calprotectin as predictors
of lung fibrosis (Hesselstrand et al., 2013), galectin 1 as
protective factor against digital vasculopathy (Yanaba et al.,
2014), Cu–Zn superoxide dismutase (Hassan et al., 2013),
erythrocyte glutathione transferase as biomarker for SSc
activity (Fabrini et al., 2013), osteonectin associated with
calcinosis (Davies et al., 2006), macrophage inhibiting factor
associated with vasculopathy (Becker et al., 2008), pulmon-
ary surfactant protein A associated with pulmonary fibrosis
(Takahashi et al., 2000) or angiotensinogen associated with
profibrotic activity (Kawaguchi et al., 2004). One can see
that only a few biomarkers summarized in the systematic
review were confirmed in the literature using immune-based
proteomics and most biomarkers have not yet been confirmed
upon independent SSc cohorts.
Interestingly, some of the biomarkers identified in the
systematic review have been described in the literature as
targets for autoantibodies in SSc. This is the example of
peroxiredoxin 1 (Iwata et al., 2007), stress-induced phospho-
protein-A (Bussone et al., 2012), vimentin, calumenin,
tropomyosin 1, heat shock protein 27, glucose-6-phosphate-
dehydrogenase, phosphatidylinositol 3-kinase (Terrier et al.,
2008), annexin A2 (Salle et al., 2008), carbonic anhidrase
(Alessandri et al., 2003), and heterogeneous ribonucleopro-
tein U (Generini et al., 2009). Future studies should include
analysis of autoantibodies directed against some of the
biomarkers listed, as their up-regulation could be secondary
to antibody secretion.
There are limitations for some of the studies included in
the systematic review, as acknowledged by the authors
themselves. First of all, most of the studies focused on a
relatively small number of patients. Secondly, in studies of
proteomic biomarkers from ‘‘in vitro’’ analysis one could
not be certain whether the biomarkers are secreted or just
expressed in the supernatant secondary to cell death and
destruction. In addition, if a biomarker is upregulated ‘‘in
vitro’’, this is not necessarily true for ‘‘in vivo’’ situations.
However, these biomarkers should also be investigated within
confirmation studies upon independent cohorts. In addition, it
is not yet clear how intracellular biomarkers were identified in
biological fluids of SSc patients. Inflammation is associated
with a degree of tissue destruction, apoptosis and necrosis,
and therefore the presence of normally intracellular proteins
in the extracellular space could be the result of such
processes. If this is the case, the presence of these proteins
could be firstly of prognostic rather than diagnostic import-
ance. Future studies should consider investigating these
proteins as candidate prognostic biomarkers.
One interesting aspect that should be flagged up is that even
if the proteomic biomarkers were confirmed using immune-
based proteomic techniques, correlations with clinical aspects
of SSc are still lacking in the literature. Studies that would
focus upon correlations between clinical SSc expressions with
a particular biomarker are therefore warranted.
An important issue to be discussed is the relationship
(correlation) between the candidate proteomic biomarkers
regarding their integration in useful and accurate predictive
models for a specific outcome or a specific clinical expression
for SSc patients. This aspect is important because it is possible
that many of the candidate biomarkers are listed to be highly
interconnected (having same physiopathological pathways,
e.g. in this systematic review they were grouped in 14
categories based on their function). Correlation between the
biomarkers could induce potential statistical issues, as collin-
earity could lead to precision loss of the predictive models. It is
therefore important to evaluate more biomarkers in the same
cohort in order to determine their collinearity and researchers
to consider what biomarkers would be best for accurate
precision models. This aspect will be a challenge as SSc
physiopathology is incompletely known and biomarkers that
are not included in the main casual pathways will be less useful
in the predictive models (mainly biomarkers that are up- or
downregulated as an epiphenomenon). However, rapid devel-
opment of high-throughput proteomic platforms will circum-
vent some of these obstacles as they can measure an
appreciable number of proteins using small amount of
biological specimen. Future good quality cohort studies that
will explore these issues will be of great value for SSc
biomarker research.
Conclusions
It is more obvious that research in SSc should focus more on
biomarker validation, as there are already valuable mass-
spectrometry proteomics studies in the literature, as acknowl-
edged by fulfillment of most recommendation criteria for
clinical proteomics biomarker reporting. In addition, bio-
marker discovery remains an important aspect as well,
especially in the growing idea of technique and reporting
standardization of mass spectrometry studies. Independent
cohort studies should test biomarkers identified using mass
spectrometry proteomics with immune-based detection assays
and try to determine the role of these candidate biomarkers,
especially for accurate fibrosis assessment. After validation,
associating biomarkers in panels would be useful because it is
known that multiple biomarkers currently achieve sensitivities
and specificities superior to that achieved by a single
biomarker. However, one should also be aware of possible
correlations between biomarkers, as some of the biomarkers
could be highly interconnected and therefore determine loss
in predictive model precision. Integrating proteomic bio-
marker panels into multiplex proteomic platforms will
improve diagnosis and prognosis of SSc patients.
Declaration of interest
The authors report no declaration of interest.
This work was conducted as a part of a PhD thesis ongoing
at UMF Carol Davila School of Medicine, Bucharest.
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Supplementary material available online
Supplementary Tables 1 and 2
DOI: 10.3109/1354750X.2014.920046 SSc biomarkers 11
Biomarkers Downloaded from informahealthcare.com by 95.76.163.65 on 05/16/14
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