Screening Of Platelet Protein Markers For Children Immune Thrombocytopenia By Platelet Proteomics

Background and Objective:immune thrombocytopenia (ITP) is an immune-mediated thrombocytopenia syndrome. It is the most common hemorrhagic disease during the Children. So far, the pathogenesis of ITP has not been completely clear, there is no conclusion about that how is the platelet function or if the platelets are active during the ITP and so on. At present, the diagnosis of ITP is still " diagnosis of exclusive method", The clinical diagnosis depends mainly on the peripheral thrombocytopenia but non-specific; Anti-platelet antibody (PAIgG) detection for the diagnosis of ITP is limited. Although its sensitivity and specificity have been improved, but it still can not be separate the " primary immune thrombocytopenia" and" secondary immune thrombocytopenia. Therefore, studies on the specific proteins which are expressed by platelet and analysis of platelet’s function in the ITP have important significance to elucidate the ITP’s pathogenesis Surface enhanced laser desorption/ionization time of flight mass spectrometry(SEDLI-TOF-MS)Was developed recently as a kind of proteomics research method. By using that technology, many raw biological specimen, such as serum, cell lysate, could be deteced with hight sensitivity and high through advantage to reflect the full view of protein in the sample.Then any changes of protein would be found to provide possiblity of early diagnosis, prognosis estimation and monitoring recurring. In our research, the platelets of ITP children, ITP treatment children, and healthy children were detected by SELDI-TOF-MS and protein chip, filtered the platelet protein associated with the pathogenesis of ITP. In the platelet proteome level to explore the pathogenesis of ITP. it was determined whether there was classification model which could be used to diagnose ITP. Methods:All the platelets samples(40ITP children,40ITP treatment children and35healthy children) were applied to the SELDI protein chip technology to generate mass pectra. Protein peaks identification dectection and clustering were performed using the Biomarker Wizard software and Biomarker pattem software, and the classification model of diagnosis ITP could be constructed by cross validation method. Then another sample(including35healthy children and40ITP children) Was double-blind tested to verify that classification model. Results:The molecular mass/charge ratio (mass-to-charge ratio, mass electron ratio, M/Z) for a range of2000to20000,40patients with ITP and35cases of normal human platelets protein analysis revealed a total of22protein expression had been statistics difference (P<0.01), in which the high expression of the protein peak in1, low expressed protein peaks of21.22distinct proteins were preliminary identificated in the protein databases and were found these protein peaks may be associated with protein synthesis and translation, signal transduction, transcriptional regulation, immune regulation, immune response anticoagulant and cell regulation. There were Phosphatidylserine decarboxylase alpha chain,50S ribosomal protein L34, Neurosecretory protein VGF, Putative uncharacterized protein encoded by MIR22HG, Urokinase-type, Humanin-like protein5, Isoform-2of-Beta-defensin110, Lactadherin, Urokinase-type plasminogen activator, P3(42), SKA3, Tight junction-associated protein1, Signal transducer CD24, Nucleoporin-62C-terminal-like protein. In addition, there were9protein peaks in the database which were identified for the unnamed. Based on the analysis of Biomarker Pattern software. the best classification model Was established which contained4protein(5170.65、7672.06m/z、3553.70m/z、2281.72m/z), achieving a correct classification. This classification tree with two lavers of five teminal nodes, Its accuracy, sensitivity and specificity were respectively96%,100%and92.5%. The double-blind test reults showed that accuracy, sensitivity and specificity to classifying the ITP were95%,96%and94%. During the ITP disease, the21differentially expressed proteins showed low expression, when the platelet count returned to normal after treatment, these low-expressed proteins were upregulated, but compared with normal control group,_except the beta-defensins110outside, still for low expression. Conclusions:SELDI protein chip combined LCM technology could filter out the high sensitivity and specificity of the platelet protein associated with ITP. The decision tree classification model may have important clinical value to the diagnosis of children with ITP.