Potential biomarkers for inherited thrombocytopenia 2 identified by plasma proteomics. (3rd April 2022)

• Record Type: Journal Article
• Authors: Wang, Xiaojie; Chen, Maoshan; Dai, Limeng; Tan, Chengning; Hu, Lanyue; Zhang, Yichi; Xiao, Yanni; Li, Fengjie; Zeng, Cheng; Xiang, Zheng; Wang, Yali; Zhang, Weiwei; Zhang, Xiaomei; Ran, Qian; Li, Zhongjun; Chen, Li
• Is Part Of: Platelets. Volume 33:Number 3(2022)
• Page Start: 443
• Page End: 450
• Publication Date: 2022-04-03
• Subjects: Data independent acquisition (DIA) -- inherited thrombocytopenia 2 (THC2) -- platelets -- proteomics; Blood platelets -- Periodicals; Blood Platelets -- Periodicals; 615.39
• DOI: 10.1080/09537104.2021.1937594
• Languages: English
• ISSNs: 0953-7104

Abstract

Abstract: Inherited thrombocytopenia 2 (THC2) is difficult to diagnose due to the lack of specific clinical characteristics and diagnostic methods. To identify potential plasma protein biomarkers for THC2, we collected the plasma samples from a THC2 family (9 THC2 and 15 non-THC2 members), enriched the medium and low abundant proteins using Proteominer and analyzed the protein profiles using the liquid chromatography-mass spectrometry in data independent acquisition mode. Initially, we detected 784 proteins in the plasma samples of this family and identified 27 up-regulated and 36 down-regulated in the THC2 group compared to the non-THC2 group (|log2 ratio| >1 and p -value <0.05). To improve the predictive power, top eight dysregulated proteins (B7Z2B4, LTF, HP, ERN1, IGHV1-8, A0A0X9V9C4, VH6DJ, and D3JV41) were selected by an area under the curve-based random forest process to construct a clinical model. Multivariate analysis with random forest and support vector machine showed that the prediction model provided high discrimination ability for THC2 diagnosis (AUC: 1.000 and 0.967, respectively). The potential plasma protein biomarkers will be tested in more THC2 patients and other thrombocytopenia patients to further validate their specificity and sensitivity.