Discovery of new rheumatoid arthritis biomarkers using the surface-enhanced laser desorption/ionization time-of-flight mass spectrometry ProteinChip approach.
OBJECTIVE
To identify serum protein biomarkers specific for rheumatoid arthritis (RA), using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) technology.


METHODS
A total of 103 serum samples from patients and healthy controls were analyzed. Thirty-four of the patients had a diagnosis of RA, based on the American College of Rheumatology criteria. The inflammation control group comprised 20 patients with psoriatic arthritis (PsA), 9 with asthma, and 10 with Crohn's disease. The noninflammation control group comprised 14 patients with knee osteoarthritis and 16 healthy control subjects. Serum protein profiles were obtained by SELDI-TOF-MS and compared in order to identify new biomarkers specific for RA. Data were analyzed by a machine learning algorithm called decision tree boosting, according to different preprocessing steps.


RESULTS
The most discriminative mass/charge (m/z) values serving as potential biomarkers for RA were identified on arrays for both patients with RA versus controls and patients with RA versus patients with PsA. From among several candidates, the following peaks were highlighted: m/z values of 2,924 (RA versus controls on H4 arrays), 10,832 and 11,632 (RA versus controls on CM10 arrays), 4,824 (RA versus PsA on H4 arrays), and 4,666 (RA versus PsA on CM10 arrays). Positive results of proteomic analysis were associated with positive results of the anti-cyclic citrullinated peptide test. Our observations suggested that the 10,832 peak could represent myeloid-related protein 8.


CONCLUSIONS
SELDI-TOF-MS technology allows rapid analysis of many serum samples, and use of decision tree boosting analysis as the main statistical method allowed us to propose a pattern of protein peaks specific for RA.
de Seny
Dominique
D
University of Liège, Centre Hospitalier Universitaire, Liege, Belgium. ddeseny@chu.ulg.ac.be
Fillet
Marianne
M
Meuwis
Marie-Alice
MA
Geurts
Pierre
P
Lutteri
Laurence
L
Ribbens
Clio
C
Bours
Vincent
V
Wehenkel
Louis
L
Piette
Jacques
J
Malaise
Michel
M
Merville
Marie-Paule
MP
D000328:Adult; D000368:Aged; D015535:Arthritis, Psoriatic / Q000097:blood / Q000175:diagnosis / Q000276:immunology; D001172:Arthritis, Rheumatoid / Q000097:blood* / Q000175:diagnosis / Q000276:immunology; D001323:Autoantibodies / Q000097:blood; D015415:Biomarkers / Q000097:blood*; D003424:Crohn Disease / Q000097:blood / Q000175:diagnosis / Q000276:immunology; D003663:Decision Trees; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010456:Peptides, Cyclic / Q000276:immunology; D040081:Protein Array Analysis / Q000379:methods* / Q000592:standards; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods* / Q000592:standards
D001323:Autoantibodies; D015415:Biomarkers; D010456:Peptides, Cyclic; C487763:cyclic citrullinated peptide