Biomarker discovery with SELDI-TOF MS in human urine associated with early renal injury: evaluation with computational analytical tools.
BACKGROUND
Urine proteomics is one of the key emerging technologies to discover new biomarkers for renal disease, which may be used in the early diagnosis, prognosis and treatment of patients. In the present study, we validated surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) for biomarker discovery in patients with mild ischaemic kidney injury.


METHODS
We used first-morning mid-stream urine samples from healthy volunteers, and from intensive care unit patients we collected urine 12-24 h after coronary artery bypass graft (CABG) surgery. Samples of 50 volunteers were mixed to establish a reference sample (master pool). Urine samples were analysed with constant creatinine levels.


RESULTS
The average intra- and interchip variation was found to be in the normal experimental range (CV of 10 to 30%). Computational analysis revealed (i) low intra-individual day-to-day variation in individual healthy volunteers; (ii) high concordance between the master pool sample and individual samples. Machine learning techniques for classification of CABG condition vs healthy patients showed that (iii) in the 3-20 kDa range, the joint activity of four protein peaks effectively discriminated the two classes, (iv) in the 20-70 kDa range, a single m/z marker was sufficient to achieve perfect separation.


CONCLUSIONS
Our results substantiate the effectiveness of Seldi-TOF MS-based computational analysis as a tool for discovering potential biomarkers in urine samples associated with early renal injury.
Vanhoutte
Kurt J A
KJ
Department of Pharmacology and Toxicology (149), Nijmegen Centre for Molecular Life Sciences/Radboud University Nijmegen Medical Centre, PO Box 9101, 6500 HB Nijmegen, the Netherlands.
Laarakkers
Coby
C
Marchiori
Elena
E
Pickkers
Peter
P
Wetzels
Jack F M
JF
Willems
Johannes L
JL
van den Heuvel
Lambert P
LP
Russel
Frans G M
FG
Masereeuw
Rosalinde
R
D000209:Acute-Phase Proteins / Q000652:urine; D000328:Adult; D000368:Aged; D000465:Algorithms; D000510:Alpha-Globulins / Q000652:urine; D015415:Biomarkers / Q000378:metabolism*; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D005260:Female; D005786:Gene Expression Regulation; D006801:Humans; D007674:Kidney Diseases / Q000175:diagnosis / Q000652:urine*; D000071068:Lipocalin-2; D054834:Lipocalins / Q000652:urine; D008297:Male; D008875:Middle Aged; D025341:Principal Component Analysis; D040901:Proteomics / Q000379:methods*; D011518:Proto-Oncogene Proteins / Q000652:urine; D012984:Software; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*; D013997:Time Factors; D016482:Urinalysis / Q000379:methods
D000209:Acute-Phase Proteins; D000510:Alpha-Globulins; D015415:Biomarkers; C489506:LCN2 protein, human; D000071068:Lipocalin-2; D054834:Lipocalins; D011518:Proto-Oncogene Proteins; C016001:alpha-1-microglobulin