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Extensive serum biomarker analysis in the prethrombotic state of recurrent spontaneous abortion.
Wu, Ying; Xin, Mingwei; Han, Qian; Wang, Jingshang; Yin, Xiaodan; He, Junqin; Yin, Chenghong.
Affiliation
  • Wu Y; Department of Traditional Chinese Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China.
  • Xin M; Department of Traditional Chinese Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China.
  • Han Q; Department of Traditional Chinese Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China.
  • Wang J; Department of Traditional Chinese Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China.
  • Yin X; Department of Traditional Chinese Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China.
  • He J; Department of Traditional Chinese Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China.
  • Yin C; Department of Traditional Chinese Medicine, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China.
J Cell Mol Med ; 25(14): 6679-6694, 2021 07.
Article in En | MEDLINE | ID: mdl-34132454
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.
Subject(s)
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Biomarkers / Abortion, Habitual / Interleukins / Epidermal Growth Factor Type of study: Prognostic_studies Limits: Adult / Female / Humans / Pregnancy Language: En Journal: J Cell Mol Med Journal subject: BIOLOGIA MOLECULAR Year: 2021 Document type: Article Affiliation country: China

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Biomarkers / Abortion, Habitual / Interleukins / Epidermal Growth Factor Type of study: Prognostic_studies Limits: Adult / Female / Humans / Pregnancy Language: En Journal: J Cell Mol Med Journal subject: BIOLOGIA MOLECULAR Year: 2021 Document type: Article Affiliation country: China