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Extensive serum biomarker analysis in the prethrombotic state of recurrent spontaneous abortion |
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| Authors: | Ying Wu Mingwei Xin Qian Han Jingshang Wang Xiaodan Yin Junqin He Chenghong Yin |
| Institution: | 1. Department of Traditional Chinese Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China
Contribution: Conceptualization (equal), Data curation (lead), Funding acquisition (equal), Investigation (lead), Writing - review & editing (lead);2. Department of Traditional Chinese Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China Contribution: Formal analysis (lead), Validation (lead);3. Department of Traditional Chinese Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China Contribution: Data curation (equal), Software (lead);4. Department of Traditional Chinese Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China Contribution: Resources (lead);5. Department of Traditional Chinese Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China Contribution: Formal analysis (equal);6. Department of Traditional Chinese Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China |
| Abstract: | 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. |
| Keywords: | biomarker prethrombotic state Quantibody array recurrent spontaneous abortion |