Abstract
BACKGROUND&OBJECTIVE FMyelodysplastic syndromes (MDS) are among the most frequent hematologic malignancies. The diagnosis of MDS can be difficult, and there is a paucity of molecular markers. The pathophysiology is still largely unknown. Therefore, we investigated whether serum proteome profiling may serve as a noninvasive platform to discover novel molecular markers for MDS and establish the predictive models that may be of help to serologic diagnosis and classification of MDS.
METHODS FSerum samples were collected from 14 MDS patients including to 8 Refractory anemia with excess blasts in transformation (RAEB) and 6 Refractory cytopenia with multilineage dysplasia (RCMD) and 18 non-MDS hematologic malignancies and 8 age- and sex-matched healthy subjects. Serum peptides were separated and purified with a purification kit of magnetic beads, using magnetic beads-based weak cation exchange chromatography (MB-WCX) and MB-IMAC Cu, bases on immobilized metal ion affinity chromatography on the surface of superparamagnetic microparticles. We generated serum proteome profiles by matrix-assisted laser desorption/ionization time of-flight mass spectrometry (MALDI-TOF- MS) and identified a profile that distinguishes MDS from non-MDS hematologic malignancies and healthy subjects.
RESULTS FA totaI of 146 effective protein peaks were detected at the molecular range of 1.02 tO 10.25 ku, Among which 7 protein peaks were different significantly among MDS patients, non-MDS hematologic malignancies and healthy subjects (P<0.05). There was also different for Peptide mass fingerprinting in MDS patients, and the samples were divided into two groups, which was identical with clinical classification about RAEB and RCMD, using 3-cross validation approach. There was significantly different expression protein between RCMD and RAEB patients, which was identified as a piece of fibrinogen peptide. The expressions of fibrinogen in RAEB subtype patients were higher than RCMD subtype patients.
CONCLUSION F Using the MALDI-TOF-MS technique may help to identify serum proteomic biomarkers related to MDS. The predictive models can discriminate MDS patients from other hematologic malignancies and healthy people effectively and help to identify MDS clinical classification. The different expression of Fibrinogen between RAEB and RCMD may suggested heterogeneity of etiopathogenisis in different subtypeof MDS.
Exploring Serological Classification Tree Model of Active Pulmonary Tuberculosis by Magnetic Beads Pretreatment and MALDI-TOF MS Analysis
