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[Diagnosis model of idiopathic thrombocytopenic purpura based on platelet differential proteome]
Pan Zhou1, Yin-Huan Ding, Peng He
1Department of Laboratorial Medicine, Affiliated Hospital of Luzhou Medical College, Luzhou, Sichuan Province, China.
Insights
A new diagnostic method using platelet protein analysis and artificial neural networks (ANN) shows promise for identifying idiopathic thrombocytopenic purpura (ITP). This quick and sensitive approach aids in ITP diagnosis.
Area of Science:
- Biochemistry
- Proteomics
- Medical Diagnostics
Background:
- Idiopathic thrombocytopenic purpura (ITP) requires accurate and efficient diagnostic methods.
- Current diagnostic approaches may lack the desired sensitivity or specificity.
Purpose of the Study:
- To develop a novel, rapid, and sensitive diagnostic technique for ITP.
- To evaluate the clinical significance of a diagnostic model based on platelet protein profiles.
Main Methods:
- Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) was used to analyze platelet lysates from 240 individuals (ITP patients, leukemia, MDS, healthy controls).
- An artificial neural network (ANN) model was constructed to identify differentially expressed proteins and establish a diagnostic signature.
- The model's efficacy was validated using a blinded test with SPSS 17.0.
Main Results:
- Five proteins showed significant differential expression (P < 0.01) between groups.
- Key protein mass-to-charge ratios (m/z) identified were 2234.30, 3476.36, and 7526.29 (highly expressed), and 4990.02 and 5152.39 (low expressed).
- The ANN diagnostic model achieved 80.6% sensitivity and 77.3% specificity, with an ROC curve area of 0.837.
Conclusions:
- The ANN model, based on platelet protein fingerprint spectra, demonstrates utility in the clinical diagnosis of ITP.
- This SELDI-TOF-MS and ANN approach offers a potentially valuable tool for improving ITP diagnostics.
Abstract:
This study was purposed to establish a new quick and simple diagnostic method with high sensitivity and good specificity for idiopathic thrombocytopenic purpura (ITP) and to evaluate its significance. 240 platelet lysates (from patients with ITP, leukemia, MDS, and healthy adults, each of 60 cases) were randomly assigned to training set (120 cases) or validation set (120 cases), all of them were detected by surface enhanced laser desorption ionization time of flight mass spectrometry (SELDI-TOF-MS), in order to identify the differentially expressed protein, the diagnostic model was established by means of artificial neural network (ANN), and was validated by blind test with SPSS 17.0. The results showed that 5 marked proteins significantly differentially expressed (P < 0.01), m/z of highly expressed proteins were 2234.30, 3476.36, and 7526.29, m/z of low expressed proteins were 4990.02 and 5152.39, respectively. The sensitivity and specificity of diagnostic model were 80.6% and 77.3% respectively. The area under the ROC curve consisting of the output value of artificial neura1 network was 0.837. Efficacy of the model was validated by means of blinded test. It is concluded that the ANN model is useful for clinical diagnosis of ITP on the basis of platelet protein fingerprint spectrum.
