Department of Traditional Chinese Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China.
J Cell Mol Med. 2021 Jul;25(14):6679-6694. doi: 10.1111/jcmm.16671. Epub 2021 Jun 16.
The prethrombotic state (PTS) is a possible cause of recurrent spontaneous abortion (RSA). The aim of this study was to identify serum biomarkers for the detection of RSA with PTS (PSRSA). A Quantibody array 440 was used to screen novel serum-based biomarkers for PSRSA/NRSA (RSA without PTS). Proteins differentially expressed in PSRSA were analysed using bioinformatics methods and subjected to a customized array and enzyme-linked immunosorbent assay (ELISA) validation. We used receiver operating characteristic to calculate diagnostic accuracy, and machine learning methods to establish a biomarker model for evaluation of the identified targets. 20 targets were selected for validation using a customized array, and seven targets via ELISA. The decision tree model showed that IL-24 was the first node and eotaxin-3 was the second node distinguishing the PSRSA and NRSA groups (an accuracy rate of 100% and an AUC of 1). Epidermal growth factor (EGF) as the node distinguished the PSRSA and NC groups (an accuracy rate of 100% and an AUC of 1). EGF as the node distinguished the NRSA and NC groups (an accuracy rate of 96.5% and an AUC of 0.998). Serum DNAM-1, BAFF, CNTF, LAG-3, IL-24, Eotaxin-3 and EGF represent a panel of promising diagnostic biomarkers to detect the PSRSA.
血栓前状态(PTS)是复发性自然流产(RSA)的可能原因。本研究旨在确定 PTS 相关 RSA(PSRSA)的血清生物标志物。使用 Quantibody 阵列 440 筛选用于 PSRSA/NRSA(无 PTS 的 RSA)的新型血清生物标志物。使用生物信息学方法分析 PSRSA 中差异表达的蛋白质,并进行定制阵列和酶联免疫吸附试验(ELISA)验证。我们使用接受者操作特征计算诊断准确性,并使用机器学习方法建立用于评估鉴定目标的生物标志物模型。使用定制阵列验证了 20 个靶标,通过 ELISA 验证了 7 个靶标。决策树模型显示,IL-24 是区分 PSRSA 和 NRSA 组的第一个节点,嗜酸性粒细胞趋化因子 3 是第二个节点(准确率为 100%,AUC 为 1)。表皮生长因子(EGF)作为节点区分 PSRSA 和 NC 组(准确率为 100%,AUC 为 1)。EGF 作为节点区分 NRSA 和 NC 组(准确率为 96.5%,AUC 为 0.998)。血清 DNAM-1、BAFF、CNTF、LAG-3、IL-24、嗜酸性粒细胞趋化因子 3 和 EGF 代表一组有前途的诊断生物标志物,用于检测 PSRSA。