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. 2017 Jun 30;16(3):2367–2378. doi: 10.3892/mmr.2017.6890

Serum biomarker analysis in patients with recurrent spontaneous abortion

Ying Wu 1, Junqin He 1, Chunyu Guo 1, Ying Zhang 1, Wei Yang 1, Mingwei Xin 1, Xinyun Liang 1, Xiaodan Yin 1, Jingshang Wang 1, Yanfeng Liu 2,✉
PMCID: PMC5547932  PMID: 28677727

Abstract

Recurrent spontaneous abortion (RSA) occurs in 1–5% of parturients. The sustained therapy and research for RSA is expensive, which is a serious issue faced by both patients and doctors. The aim of the present study was to detect protein expression profiles in the serum of RSA patients and healthy controls, and to identify potential biomarkers for this disease. A 1,000-protein microarray consisting of a combination of Human L-507 and L-493 was used. The microarray data revealed that eight serum protein expression levels were significantly upregulated and 143 proteins were downregulated in RSA patients compared with the healthy controls. ELISA individually validated 5 of these 151 proteins in a larger cohort of patients and control samples, demonstrating a significant decrease in insulin-like growth factor-binding protein-related protein 1 (IFGBP-rp1)/IGFBP-7, Dickkopf-related protein 3 (Dkk3), receptor for advanced glycation end products (RAGE) and angiopoietin-2 levels in patients with RSA. Sensitivity and specificity analyses were calculated by a receiver operating characteristics curve, and were revealed to be 0.881, 0.823, 0.79 and 0.814, with diagnostic cut-off points of 95.44 ng/ml for IFGBP-rp1, 32.84 ng/ml for Dkk3, 147.27 ng/ml for RAGE and 441.40 ng/ml for angiopoietin-2. The present study indicated that these four proteins were downregulated in RSA samples and may be useful as biomarkers for the prediction and diagnosis of RSA. Subsequent studies in larger-scale cohorts are required to further validate the diagnostic value of these markers.

Keywords: recurrent spontaneous abortion, serum biomarker, antibody array

Introduction

Recurrent spontaneous abortion (RSA), also referred to as recurrent miscarriage, habitual abortion or recurrent pregnancy loss, is defined by more than three consecutive miscarriages prior to 20 gestational weeks (1,2). RSA occurs in 1–5% of women during pregnancy (3). The cause of RSA remains unknown; thus, continuing clinical and laboratory investigations are required (4,5). Previous studies have reported that various etiologic factors are involved in certain RSA cases; including chromosome abnormalities, endocrine diseases, uterine abnormalities, placental anomalies, hormonal problems, thrombophilia, infections, nutritional disorders, autoimmune disease and anatomy (6–8). The etiology of RSA remains to be fully elucidated despite numerous studies investigating the above factors. Early prediction of the potential risk of RSA is required to increase live birth rates in patients with RSA (9).

Biomarkers are currently widely used to refine diagnoses, predict disease and monitor the effects of treatment (10). It is established that the human proteome regulates cellular function and determines the phenotype; thus, the identification of relevant proteins is likely to reveal reliable biomarkers for predicting disease (11). A range of potential biomarkers for RSA have been previously reported. Stortoni et al (12) reported that expression levels of thrombomodulin were reduced by 45% in patients with RSA compared with healthy individuals, as determined by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Bao et al (13) determined by RT-qPCR, western blot analysis and immunohistochemistry that serum Dickkopf-related protein (Dkk) 1 levels were increased in RSA patients compared with controls. Additional studies are required to validate these potential biomarkers and their prognostic value. Identifying novel RSA biomarkers may improve the diagnosis, safety and efficacy of current therapies for RSA. As one of the most intensely studied protein families in biomedical science, cytokines have been widely investigated as potential disease biomarkers (14). The introduction of high-throughput and high-specificity detection of complex proteins at picomolar and femtomolar quantities, and antibody arrays, are now widely used for mining complex proteomes (15), facilitating simultaneous screening of numerous secreted signal proteins in complex biological samples (16). However, to the best of our knowledge, no previous study has identified serum RSA biomarkers using antibody array technology. Therefore, the present study used a RayBio® Label-Based (L-Series) Human Antibody Array 1000 Membrane kit (RayBiotech, Inc., Norcross, GA, USA) to identify reliable biomarkers for the prediction of RSA.

Patients and methods

Patients and controls

From January 2014 to March 2015, a total of 60 Chinese patients with a history of RSA were recruited as the patient group from the Department of Traditional Chinese Medicine at the Beijing Obstetrics and Gynecology Hospital. They had normal endocrine levels, and their partners had normal spermatogenesis and sperm function. ‘Blood stasis’ syndrome (BSS, also known as Xueyu zheng in Chinese) is characterized in traditional medicine as ‘pain that occurs in a fixed location, dark-purple face or tongue, bleeding, blood spots under the skin, and an astringent pulse’ among other features (17). The concept of blood stasis has been interpreted, changed and developed systematically since ancient times (18). All 60 RSA patients exhibited the ‘blood stasis’ features described above at the time of study. Patient characteristics, including age at diagnosis, gravidities, number of child births and timing of spontaneous abortion, are summarized in Table I. For the control group, 20 Chinese females who had experienced full-term pregnancies were recruited from the Department of Traditional Chinese Medicine at the Beijing Obstetrics and Gynecology Hospital.

Table I.

Characteristics of 60 patients with recurrent spontaneous abortion.

Characteristic Value
Age at diagnosisa 30±2.8
Graviditiesa 3±0.5
No. of childbirths
  1b 5 (8%)
  0b 55 (92%)
Spontaneous abortionsa 3±0.5
  2b 1 (2%)
  1b 12 (20%)
  0b 47 (78%)
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a

Data are expressed as the mean ± standard deviation.

b

Data are expressed as the number of patients (% of total).

Ethical approval and sample collection

All participants signed informed consent forms prior to participation. The present study was approved by the Ethics Committee of the Beijing Obstetrics and Gynecology Hospital, Capital Medical University (Beijing, China; approval no. 2014-KY-001). Whole blood samples were collected from each participant. Serum was collected following blood centrifugation at 550 × g for 10 min at 4°C, and stored at −80°C. The sera of 23 RSA patients and 10 healthy subjects were pooled into 6 samples followed by standard processing (19). The samples included those that could be classified as ‘blood stasis’ 1, 2 and 3, ‘non-blood stasis’ 1, 2, and 3, and controls 1–6. The order of mixing is presented in Table II. All mixtures were obtained by mixing equal volumes of sera.

Table II.

Pooling of serum samples.

Pooled serum sample Original sample
Patients with RSA
  Blood stasis group 1 (thrombus 1) A1, A2, A3, A4
  Blood stasis group 2 (thrombus 2) A5, A6, A7, A8
  Blood stasis group 3 (thrombus 3) A9, A10, A11, A12
  Non-blood stasis group 1 (non-thrombus 1) C1, C2, C3, C4
  Non-blood stasis group 2 (non-thrombus 2) C5, C6, C7, C8
  Non-blood stasis group 3 (non-thrombus 3) C9, C10, C11
Control
  1 E1, E2, E3
  2 E4, E5, E6
  3 E7
  4 E8
  5 E9
  6 E10
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RSA, recurrent spontaneous abortion.

Antibody array assay

The 12 samples described above were assayed for the relative expression of 1,000 human proteins. The target proteins included cytokines, chemokines, adipokines, growth factors, angiogenic factors, proteases, soluble receptors and soluble adhesion molecules. A RayBio® Label-Based (L-Series) Human Antibody Array 1,000 Membrane kit (consisting of a combination of Human L-507 and L-493) was used for protein detection in accordance with the manufacturer's protocol. The signals were scanned at a wavelength of 532 nm using an InnoScan 300 Microarray Scanner (Innopsys, Carbonne, France; resolution, 10 µm) and analyzed using RayBio Analysis Tool software (AAH-BLG-1-SW and AAH-BLG-2-SW; RayBiotech, Inc.).

Detection of protein levels by ELISA

As determined by microarray analysis, serum markers with significant differences in expression levels between patients and healthy individuals were detected in 60 patients and 20 controls using ELISA kits (ELH-TRAPPIN2, ELH-IGFBPRP1, ELH-RAGE, ELH-DKK3 and ELH-Angiopoietin-2; RayBiotech, Inc.) according to the manufacturer's protocol. Trappin-2, insulin-like growth factor-binding protein-related protein 1 (IGFBP-rp1)/IGFBP-7, receptor for advanced glycation end products (RAGE), Dkk3, and angiopoietin-2 levels were detected. Serum samples were incubated at room temperature. Following washing with wash buffer, a prepared biotinylated antibody was added into the microplate to capture the target protein. Following this, horseradish peroxidase-conjugated streptavidin was used to bind with biotin from the biotinylated antibody. Finally, 1-Step 3,3′,5,5′-tetramethylbenzidine-ELISA substrate solution was added followed by stop solution, and absorbance was measured at a wavelength of 450 nm by absorbance microplate reader ELx800 (BioTek Instruments, Inc., Winooski, VT, USA).

Statistical analysis and bioinformatics

All array data analyses were performed using RayBio Analysis Tool software. Biostatistics and bioinformatics analysis included discriminatory protein analysis and data mining cluster analysis. Statistical differences between two groups were determined by Student's t-test. Fold change values of proteins were used as indicators of relative expression levels. Data mining cluster analysis was used to identify potential biomarkers by clustering all relevant proteins according to the similarity of their expression profiles using Cluster software version 3.0 (http://cluster2.software.informer.com/3.0). ELISA data was analyzed using SigmaPlot software version 12.0 (Systat Software, Inc., San Jose, CA, USA). T- and F-tests were used to analyze ELISA quantification. The receiver operating characteristics curve (ROC) method was used to assess sensitivity and specificity of potential biomarkers using SPSS software version 13.0 (SPSS, Inc., Chicago, IL, USA). P<0.05 was considered to indicate a statistically significant difference.

Results

Analysis of antibody microarrays

A total of 1,000 proteins were measured in the serum mixture using the microarray. The spectra of 1,000 proteins from eight samples are presented in Fig. 1. The results demonstrated that 151 proteins had significantly different expressions between the two groups. Of these differential proteins, eight were significantly upregulated, and 143 proteins were downregulated in RSA patients compared with controls (Table III). Fig. 2 presents are boxplots of the fluorescence signal values of eight differential proteins, selected for signal strength, fold changes and clinical significance. Serum mixture samples were arranged by similarities in the abundance of these 151 markers in the sera clustering algorithm, which produced two clusters that contained patients and healthy individuals (Fig. 3).

Figure 1.

Figure 1.

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Protein spectra from RayBio L-Series Human 507 (507 proteins) and 493 (493 proteins) antibody arrays. Representative images from human antibody arrays demonstrating the reactivity of pooled serum samples to arrays L series (1,000 proteins) in healthy controls and RSA patients. Each protein was measured in duplicate. A total of eight of significantly different factors on the microarrays are marked in elliptical boxes. IGFBP-rp1/IGFBP-7, insulin-like growth factor-binding protein-related protein 1/insulin-like growth factor-binding protein 7; Dkk3, Dickkopf-related protein 3; RAGE, receptor for advanced glycation end products; RSA, recurrent spontaneous abortion; TOPORS, topoisomerase I binding, arginine/serine-rich, E3 ubiquitin protein ligase; C2, complement C2; RECK, reversion-inducing-cysteine rich protein with kazal motifs.

Table III.

A total of 151 proteins with significantly different expression levels between patients with RSA and controls.

RSA Control RSA vs. control
Target Mean SD Mean SD F-test t-test P-value Fold-change
A2M 4,650.558 373.053 6,374.645 269.687 0.494 0.000 0.730
ADAMTS-10 7,862.456 1,306.692 13,925.383 550.378 0.081 0.000 0.565
ADAMTS-15 2,999.115 479.112 5,705.655 1,924.875 0.008 0.017 0.526
ADAMTS-5 558.209 250.695 1,466.103 483.403 0.176 0.002 0.381
ADAMTS-L2 20,775.755 2,676.424 27,565.213 1,305.789 0.141 0.000 0.754
ALK 7,976.603 1,428.122 13,199.048 2,632.151 0.206 0.002 0.604
Angiopoietin-2 20,789.029 4,292.976 48,570.398 4,977.529 0.753 0.000 0.428
ApoC2 14,305.316 2,066.666 19,754.508 2,309.965 0.813 0.002 0.724
ApoH 4,898.886 462.884 7,760.884 1,065.437 0.091 0.000 0.631
ApoM 1,502.749 273.114 3,924.876 654.067 0.078 0.000 0.383
APP 12,633.899 1,318.521 19,428.245 5,101.616 0.010 0.021 0.650
Axl 1,510.055 1,117.320 4,936.198 495.598 0.099 0.000 0.306
BAF57 1,181.342 265.140 1,906.967 361.301 0.513 0.003 0.619
BAFF R/TNFRSF13C 130.169 152.078 1,362.334 162.415 0.889 0.000 0.096
Bax 15,217.862 3,242.826 38,315.362 7,712.389 0.080 0.000 0.397
BDNF 13,455.044 6,688.395 25,250.436 5,906.534 0.792 0.009 0.533
β 2M 1,320.861 133.263 1,910.310 294.761 0.106 0.001 0.691
BIK 311.407 134.485 1,179.513 269.458 0.153 0.000 0.264
BMP-3 10,702.264 3,851.243 28,110.700 15,617.736 0.008 0.041 0.381
BMP-3b/GDF-10 124.436 41.431 347.417 122.473 0.033 0.005 0.358
BMP-4 901.991 2,56.578 1,767.609 343.897 0.536 0.001 0.510
BMPR-IB/ALK-6 23,780.461 9648.386 62,589.402 6,422.893 0.393 0.000 0.380
BTC 11,944.615 4,921.916 21,760.107 3,620.813 0.517 0.003 0.549
C2 22,604.021 2,719.955 29,647.219 2,700.060 0.988 0.001 0.762
C5/C5a 12,737.991 1,525.086 22,749.974 7,304.236 0.004 0.019 0.560
Calsyntenin-1 1,911.570 607.215 4,505.218 691.477 0.782 0.000 0.424
CD40/TNFRSF5 1,101.031 337.593 3,397.425 678.353 0.152 0.000 0.324
Chordin-Like 1 1,391.942 585.338 3,128.589 566.356 0.944 0.000 0.445
CNTF R α 77,532.324 10,164.182 114,914.734 12,924.497 0.611 0.000 0.675
Contactin-1 3,977.884 438.734 6,093.532 1,458.926 0.020 0.015 0.653
Cripto-1 8,176.343 2,831.476 18,724.736 4,786.781 0.274 0.001 0.437
CRTH-2 34,856.201 6,547.851 55,742.907 8,503.753 0.580 0.001 0.625
CXCR4 (fusin) 3,702.606 1,090.358 11,095.207 6,134.514 0.002 0.031 0.334
Dkk-3 14,387.266 3,452.455 31,716.357 11,437.946 0.020 0.012 0.454
DLL4 1,759.891 629.238 3,330.977 898.310 0.453 0.006 0.528
EDAR 1,240.627 223.958 4,773.756 1,971.378 0.000 0.007 0.260
EGF R/ErbB1 11,026.167 1,733.072 15,758.956 2,371.519 0.508 0.003 0.700
EG-VEGF/PK1 29,916.922 4,877.762 51,349.446 16,211.012 0.020 0.022 0.583
EMAP-II 10,666.342 2,600.714 19,708.795 3,896.837 0.395 0.001 0.541
EphB4 1,962.256 406.898 5,161.814 945.891 0.088 0.000 0.380
ErbB2 4,267.172 1,130.288 15,536.343 9,241.970 0.000 0.030 0.275
ESAM 2,854.186 495.592 5,990.106 2,581.532 0.002 0.030 0.476
FAM3B 1,270.946 447.324 4,455.137 885.200 0.160 0.000 0.285
FGF R4 3,992.796 1,049.190 12,794.810 7,288.004 0.001 0.031 0.312
FGF R5 2,826.176 1,013.809 7,878.562 1,003.601 0.983 0.000 0.359
FGF-19 1,641.777 369.438 4,442.636 778.147 0.128 0.000 0.370
FGF-9 6,413.361 1,443.488 22,180.535 5,185.520 0.014 0.000 0.289
FGFR1 11,978.622 1,527.435 16,683.655 4,343.023 0.039 0.045 0.718
FGFR2 13,703.293 2,914.828 23,307.209 7,653.038 0.054 0.017 0.588
Ficolin-3 3,374.750 486.123 8,120.462 4,212.321 0.000 0.040 0.416
Follistatin-like1 2,481.289 721.297 7,152.976 1,161.224 0.319 0.000 0.347
Galectin-1 1,435.877 634.069 3,694.218 803.285 0.616 0.000 0.389
Galectin-3BP 10,655.461 1,406.056 13,522.242 1,910.307 0.517 0.014 0.788
Gas1 4,530.505 1,493.529 7,110.508 1,228.339 0.678 0.008 0.637
GASP-1/WFIKKNRP 85,406.876 7,073.932 128,207.327 24,054.577 0.018 0.006 0.666
GATA-3 7,625.978 591.277 13,276.727 2,655.282 0.005 0.003 0.574
GCP-2/CXCL6 847.677 514.092 2,243.977 1,012.301 0.163 0.013 0.378
GLO-1 997.491 290.387 2,106.667 501.997 0.255 0.001 0.473
Glucagon 61,025.896 9,853.803 128,506.268 35,031.652 0.015 0.004 0.475
GluT2 11,546.660 704.259 37,514.070 2,261.457 0.023 0.000 0.308
Glypican 3 1,389.140 249.379 2,554.322 649.947 0.056 0.002 0.544
Glypican 5 15,529.644 2,266.574 25,519.026 7,257.053 0.023 0.018 0.609
GPX1 2,008.854 557.930 4,383.148 782.893 0.475 0.000 0.458
GPX3 3,009.541 1,283.488 5,083.531 927.399 0.493 0.009 0.592
GRP78 2,266.940 302.999 4,387.312 1,150.085 0.011 0.005 0.517
Hemopexin 725.265 151.625 3,871.951 913.350 0.001 0.000 0.187
HRG-α 5,321.121 1,385.473 7,078.008 834.906 0.291 0.024 0.752
HSP10 26,971.939 3,802.952 43,781.370 10,058.305 0.052 0.003 0.616
I-309 4,283.951 2,116.728 13,856.485 3,913.389 0.204 0.000 0.309
IBSP 7,712.034 986.133 10,133.874 1,761.061 0.229 0.015 0.761
IGFBP-4 1,453.473 684.730 4,146.757 1,511.595 0.107 0.003 0.351
IGFBP-rp1/IGFBP-7 7,523.924 2,135.058 16,380.247 2,502.068 0.736 0.000 0.459
IGF-II 65,869.138 5,912.640 94,321.230 10,392.485 0.241 0.000 0.698
IL-1 ra 361,872.941 34,398.268 563,878.996 45,414.321 0.011 0.011 0.642
IL-13 R α1 6,360.695 1,012.833 24,083.997 13,484.474 0.000 0.023 0.264
IL-17C 2,345.533 541.033 7,048.395 748.914 0.493 0.000 0.333
IL-18 R β/AcPL 1,935.580 858.981 5,832.686 1,358.705 0.337 0.000 0.332
IL-29 8,733.803 1,800.279 24,866.993 5,813.232 0.022 0.001 0.351
IL-31 1,970.735 687.942 5,958.295 784.836 0.779 0.000 0.331
IL-31 RA 1,182.894 372.281 3,207.266 506.214 0.516 0.000 0.369
IL-33 2,441.589 430.891 3,847.273 1,061.515 0.070 0.013 0.635
IL-6 R 3,540.308 909.773 6,566.421 1,881.755 0.137 0.005 0.539
IL-8 25,002.761 6,204.334 34,829.214 6,706.528 0.869 0.025 0.718
Kallikrein 14 2,979.489 615.746 5,825.747 1,585.646 0.058 0.002 0.511
LBP 3,409.164 463.893 13,342.910 8,052.946 0.000 0.029 0.256
LIF R α 13,797.957 2,293.325 27,604.197 8,923.296 0.010 0.012 0.500
LIF 332.827 242.463 1,628.345 875.661 0.014 0.014 0.204
LIGHT/TNFSF14 2,092.869 917.268 6,360.354 1,227.569 0.538 0.000 0.329
Lipocalin-1 20,832.310 4,151.253 34,570.502 9,172.736 0.107 0.007 0.603
Livin 2,707.067 795.904 4,194.195 745.424 0.889 0.007 0.645
LRG1 11,113.403 863.592 28,581.729 14,531.460 0.000 0.032 0.389
Lymphotoxin β R/TNFRSF3 788.596 380.913 3,291.572 764.370 0.153 0.000 0.240
M-CSF 28,394.735 5,351.774 41,766.017 11,016.348 0.139 0.023 0.680
Midkine 3,267.246 433.600 5,032.121 1,617.032 0.012 0.044 0.649
MIF 3,951.056 885.814 6,986.334 1,120.792 0.618 0.000 0.566
MIP-1α 50,222.493 12,359.813 94,756.426 25,311.008 0.142 0.003 0.530
MMP-11/Stromelysin-3 54,153.970 12,843.620 69,764.345 7,386.564 0.250 0.027 0.776
MMP-16/MT3-MMP 11,330.829 2,524.054 26,764.023 11,916.806 0.004 0.024 0.423
MMP-8 52,067.139 3,933.472 70,211.908 7,728.075 0.165 0.000 0.742
MSP α Chain 13,201.992 2,412.896 25,409.389 4,525.441 0.194 0.000 0.520
NEP 1,581.827 658.480 3,958.249 666.439 0.980 0.000 0.400
NM23-H1/H2 550.675 189.223 2,651.125 1,022.967 0.002 0.004 0.208
Orexin B 43,718.747 7,996.118 74,138.593 18,347.514 0.092 0.004 0.590
Osteoactivin/GPNMB 2,895.689 312.735 5,540.870 1,872.568 0.001 0.017 0.523
PD-1 2,591.490 389.734 4,141.350 1,038.516 0.051 0.007 0.626
PDGF-C 9,264.920 2,881.398 24,377.899 9,157.178 0.024 0.008 0.380
PDGF-D 2,883.630 396.751 5,130.131 907.360 0.093 0.000 0.562
PDX-1 2,270.140 363.742 3,981.057 824.869 0.097 0.001 0.570
PEPSINOGEN I 2,439.074 824.492 6,093.801 1,335.963 0.313 0.000 0.400
Persephin 2,515.448 1,011.303 4,523.807 838.775 0.691 0.004 0.556
PGRP-S 12,316.910 730.024 16,966.564 1,180.649 0.315 0.000 0.726
PIM2 1,865.699 437.191 4,879.191 1,260.593 0.036 0.001 0.382
PKM2 2,655.795 1,099.697 4,528.436 478.749 0.092 0.003 0.586
RAGE 9,020.357 1,947.254 25,325.305 10,739.884 0.002 0.013 0.356
RANK/TNFRSF11A 2,020.058 587.942 4,020.679 833.497 0.462 0.001 0.502
RECK 5,084.724 891.920 11,081.323 3,186.035 0.014 0.005 0.459
RELT/TNFRSF19L 31,871.125 4,886.989 61,866.224 17,568.797 0.014 0.007 0.515
ROBO4 66,732.387 11,925.740 100,118.428 17,349.652 0.430 0.003 0.667
S100A10 1,189.482 653.171 4,146.007 336.156 0.171 0.000 0.287
S100A4 4,024.174 328.979 5,366.717 482.437 0.421 0.000 0.750
S100A6 3,485.252 541.367 6,023.360 1,041.092 0.178 0.000 0.579
Serpin A8 1,755.946 304.447 3,270.617 704.644 0.089 0.001 0.537
Serpin A9 1,258.179 327.453 2,710.239 999.220 0.029 0.015 0.464
Smad 1 1,028.273 540.825 3,720.431 748.020 0.494 0.000 0.276
Smad 7 34,565.690 2,075.517 55,551.432 18,891.474 0.000 0.042 0.622
Smad 8 6,886.663 2,599.832 15,373.512 4,688.229 0.221 0.003 0.448
SOST 3,117.271 461.906 6,218.660 1,701.605 0.012 0.006 0.501
Spinesin 20,626.782 4,848.625 38,828.941 4,444.642 0.853 0.000 0.531
Syndecan-1 1,772.888 379.277 3,778.232 1,215.352 0.023 0.008 0.469
Thrombospondin-4 10,921.569 2,002.073 23,198.652 9,994.362 0.003 0.029 0.471
TIM-1 2,132.294 259.727 4,438.018 697.608 0.049 0.000 0.480
TIMP-3 4,008.775 1,259.727 6,988.724 1,853.036 0.417 0.009 0.574
TRADD 127,030.588 28,309.796 222,266.652 75,285.718 0.051 0.016 0.572
TRAIL R2/DR5/TNFRSF10B 17,190.463 4,094.960 32,861.735 13,104.866 0.023 0.032 0.523
Trappin-2 5,865.048 978.164 19,388.585 11,625.715 0.000 0.036 0.303
TROY/TNFRSF19 2,324.349 786.973 5,012.391 1,945.216 0.069 0.011 0.464
TRPC1 2,436.721 681.450 6,693.017 2,511.517 0.012 0.008 0.364
TSLP 1,853.969 303.884 5,110.938 2,119.578 0.001 0.013 0.363
TSLP R 1,905.470 965.346 6,488.242 1,625.517 0.277 0.000 0.294
Ubiquitin+1 17,529.029 5,002.370 34,561.192 16,122.797 0.023 0.049 0.507
uPA 52,463.526 6,879.542 94,616.706 25,331.681 0.012 0.008 0.554
VEGF-D 24,467.055 5,668.534 52,501.169 6,836.652 0.691 0.000 0.466
WISP-1/CCN4 2,330.112 1,166.842 5,516.779 2,727.490 0.086 0.025 0.422
GASP-2/WFIKKN 71,246.693 3,344.127 31,422.325 19,817.529 0.001 0.004 2.267
IL-1 F5/FIL1δ 30,158.276 6,083.967 13,083.494 8,056.469 0.553 0.002 2.305
IL-28A 71,058.941 7,420.008 30,883.742 18,108.416 0.072 0.001 2.301
Kallikrein 6 18,588.432 1,695.500 10,786.937 3,766.885 0.104 0.001 1.723
NGF R 42,876.389 1,905.084 1,8740.985 13,128.177 0.001 0.006 2.288
NrCAM 73,805.167 6,435.403 5,1415.054 8,782.973 0.511 0.001 1.435
TOPORS 49,264.099 3,557.276 3,4240.228 8,383.960 0.083 0.002 1.439
VEGF R2 (KDR) 37,152.074 4,541.513 1,2904.085 9,743.845 0.119 0.000 2.879
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SD, standard deviation; RSA, recurrent spontaneous abortion.

Figure 2.

Figure 2.

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Boxplots of differential serum proteins between RSA patients and controls. P<0.05, RSA vs. control for all presented proteins. ANG-2, angiopoietin 2; IGFBP-rp1/IGFBP-7, insulin-like growth factor-binding protein-related protein 1/insulin-like growth factor-binding protein 7; Dkk3, Dickkopf-related protein 3; RAGE, receptor for advanced glycation end products; RSA, recurrent spontaneous abortion; TOPORS, topoisomerase I binding, arginine/serine-rich, E3 ubiquitin protein ligase; C2, complement C2; RECK, reversion-inducing-cysteine rich protein with kazal motifs.

Figure 3.

Figure 3.

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Cluster map of protein expression levels of the 151 proteins in 12 mixed serum samples. The signal values of the 151 proteins from microarray analyses were used to prepare the cluster map. Red shades indicate higher expression levels, green shades indicate lower expression levels, and black shades indicate median expression levels.

Validation of microarray data by ELISA

A total of five of the 151 proteins were selected for validation assay in 60 RSA and 20 control samples. Serum levels of trappin-2, IGFBP-rp1/IGFBP-7, RAGE, Dkk3 and angiopoietin-2 were selected to be measured by ELISA based on the results from the microarray experiments, previous reports on serum biomarkers in RSA and the availability of commercial test kits. Levels of IGFBP-rp1/IGFBP-7, Dkk3, RAGE and angiopoietin-2 were downregulated in RSA patients compared with healthy controls, which was consistent with the microarray results (P<0.05; Table IV and Fig. 4).

Table IV.

ELISA analysis of cytokine levels in the serum of patients with RSA and healthy controls.

Patients vs. control
Cytokine RSA Control F-test t-test P-value Fold-change
Trappin-2 429.17±125.17 453.26±132.34 0.718 0.473 0.947
IGFBP-rp1/IGFBP-7   86.94±16.49 115.63±20.12 0.246 0.000a 0.752
RAGE   91.29±44.28 163.64±76.99 0.001 0.001a 0.558
Dkk3   28.16±6.22   38.96±10.05 0.005 0.000 0.723
Angiopoietin-2 461.34±484.38 887.72±576.22 0.312 0.002a 0.520
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a

P<0.05, RSA vs. control. Data are expressed as the mean ± standard deviation. IGFBP-rp1/IGFBP-7, insulin-like growth factor-binding protein-related protein 1/insulin-like growth factor-binding protein 7; Dkk3, Dickkopf-related protein 3; RAGE, receptor for advanced glycation end products; RSA, recurrent spontaneous abortion.

Figure 4.

Figure 4.

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Validation of five differentially expressed proteins obtained from microarray analysis, and validated by ELISA. Concentrations of these factors in serum samples obtained from RSA patients and healthy controls were calculated using the four parameters method. IGFBP-rp1/IGFBP-7, insulin-like growth factor-binding protein-related protein 1/insulin-like growth factor-binding protein 7; Dkk3, Dickkopf-related protein 3; RAGE, receptor for advanced glycation end products; RSA, recurrent spontaneous abortion.

Analysis of sensitivity and specificity of serum biomarkers for RSA

To validate whether IGFBP-rp1/IGFBP-7, Dkk3, RAGE and angiopoietin-2 may be used as biomarkers for predicting RSA, ROC curves were used to analyze sensitivity and specificity. Area-under-ROC-curve values for IGFBP-rp1/IGFBP-7 (Fig. 5A), Dkk3 (Fig. 5B), RAGE (Fig. 5C) and angiopoietin-2 (Fig. 5D) cytokines were 0.881, 0.823, 0.79 and 0.814, respectively. IGFBP-rp1/IGFBP-7 had a sensitivity of 95% and specificity of 78.33%. Dkk3 had a sensitivity of 80% and specificity of 83.33%. RAGE had a sensitivity of 65% and specificity of 86.70%. Angiopoietin-2 had a sensitivity of 90% and specificity of 64.40%. All these were deemed suitable biomarkers for the prediction of RSA.

Figure 5.

Figure 5.

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ROC curve analysis for the four upregulated serum cytokines as validated by ELISA. The area under the ROC curve (AUC) indicates the mean sensitivity of the biomarkers (A) IGFBP-rp1, (B) Dkk3, (C) RAGE and (D) angiopoietin-2 (Fig. 5D). 0.5≤AUC≤1, the biomarker is strongly differential between patients and controls; AUC≤0.5, no predictive value. ROC, receiver operating characteristics curve; IGFBP-rp1/IGFBP-7, insulin-like growth factor-binding protein-related protein 1/insulin-like growth factor-binding protein 7; Dkk3, Dickkopf-related protein 3; RAGE, receptor for advanced glycation end products; RSA, recurrent spontaneous abortion.

Discussion

Potential biomarkers of RSA have previously been reported. Khonina et al (20) investigated whether mixed lymphocyte reaction blocking factor may be used as an indicator of the efficacy for immunotherapy with paternal lymphocytes in females with RSA. Metwally et al (21) performed a proteomic analysis of obese and overweight women with RSA by 2-D gel electrophoresis, principle component analysis and mass spectrometry, and demonstrated that RSA patients exhibit a significant increase in haptoglobin expression. Ibrahim et al (22) demonstrated that pentraxin-3 indicates the presence of abnormally exaggerated intrauterine inflammation that may cause pregnancy failure in females with unexplained RSA. Kim et al (23) identified RSA-associated factors in human blood samples by 2-D gel electrophoresis, and analyzed spots samples with matrix-assisted laser desorption/ionization-time of flight/mass spectrometry, and reported that in RSA patients, inter-α-trypsin inhibitor heavy chain family member 4 (ITI-H4) expression was low and exhibited a molecular weight of 120 kDa in controls; however, ITI-H4 was expressed at higher levels and at a modified molecular weight of 36 kDa in the RSA patient group. This indicated that ITI-H4 may be used as biomarker of RSA.

The present study used antibody array technology for a primary screening of RSA biomarkers on pooled samples. The array results revealed that the levels of eight cytokines were significantly increased in the RSA patient group compared with controls, and the levels of 143 of the tested 1,000 proteins were significantly reduced in the RSA patient group compared with controls. A total of 5 proteins, trappin-2, IGFBP-rp1/IGFBP-7, Dkk3, RAGE and angiopoietin-2, were selected for ELISA validation assay in a larger cohort of patient and control subjects. ELISA results for these proteins were in accordance with the array results. Sensitivity and specificity analysis by ROC revealed that these four cytokines may be used as biomarkers of RSA.

To the best of our knowledge, the association between IGFBP-rp1/IGFBP-7, Dkk3 and angiopoietin-2, and RSA has not been reported. However, an isoform of the RAGE protein, sRAGE, has been reported to be associated with RSA (24).

IGFBP-rp1/IGFBP-7

IGFs, which have characteristics of tissue growth factors and circulating growth hormones, are potent mitogens and anti-apoptotic agents (25). IGFs include the hormones IGF-I and -II and their corresponding receptors, and the IGFBPs (26). The IGFBP superfamily includes six members (IGFBP-1-6) and 10 associated proteins (IGFBP-rp1-10) (27). IGFBP-7 has been demonstrated to be a tumor suppressor in a variety of cancers. Benatar et al (28) reported that treatment with IGFBP-7 may have therapeutic potential for triple-negative breast cancer. Liu et al (29) demonstrated that IGFBP-7 was downregulated in gastric cancer, and that it may be used as an indicator of poor prognosis in patients with gastric cancer. IGFBP-7 has additionally been proposed as a novel biomarker for assessing the risk of acute kidney injury (30) and heart failure with reduced ejection fraction, and has been demonstrated to have links to the presence and severity of echocardiographic parameters of abnormal diastolic function (31).

Dkk3

The Wnts are an evolutionarily conserved family of secreted glycoproteins characterized by numerous conserved cysteine residues (32). The Dkk proteins are secreted Wnt inhibitors, inducing removal of the Wnt co-receptor low-density lipoprotein receptor-related protein, and consist of four primary members in vertebrates (Dkk1-4) (33,34). Dkk-3 is downregulated in various types of cancer cells. Loss of Dkk3 protein expression is associated with poor prognoses in patients with gastric cancer, indicating that it may be a biomarker for predicting lymph node involvement in these patients (35). Dkk3 has recently been implicated in clear cell renal cell carcinoma, and may present a novel molecular target for its diagnosis and treatment (36). Additionally, Dkk3 may represent a therapeutic target for the treatment of heart failure following myocardial infarction (37).

RAGE

RAGE is a cell-surface receptor that interacts with AGEs, and is a member of the immunoglobulin superfamily (38). RAGE activation via its multiple ligands, including S100 calcium-binding protein (S100A) 4 (39), high mobility group box 1 protein (40) and amyloid-β protein (41), serves important roles in certain diseases. Dahlmann et al (42) demonstrated that the activity of S100A4-RAGE induces RAGE-dependent increases in the migratory and invasive capabilities of colorectal cancer cells. Guo et al (43) identified RAGE as a potential prognostic biomarker in renal cell carcinoma. RAGE/S100A7 signaling has been demonstrated to have a functional role in linking inflammation to aggressive breast cancer development; therefore, RAGE expression is currently regarded as a potential biomarker for triple-negative breast cancer (44). Additionally, overexpression of RAGE may be a useful marker to predict gastric cancer progression (45).

Angiopoietin-2

As a member of the angiopoietin family, angiopoietin-2 has complex and unique roles in regulating angiogenesis, and has additional unconventional functions, including stimulating tumor angiogenesis, invasion and metastasis via Tie2-independent signaling pathways, involving integrin-mediated signaling. Therefore, angiopoietin-2 may have great potential as a therapeutic target, prognostic marker and inhibitor of human cancer (46). Angiopoietin-2 is expressed during vascular remodeling, thus preventing vascular stability (47). A study by Morrissey et al (48) demonstrated that angiopoietin-2 inhibition impeded tumor growth of LuCaP 23.1 prostate cancer xenografts, and suggested that angiopoietin-2 inhibition in combination with other treatments is a potential therapy for metastatic disease patients. Calfee et al (49) reported that lowering plasma angiopoietin-2 with fluid conservative therapy may be beneficial, in part by decreasing endothelial inflammation. Goede et al (50) demonstrated that serum angiopoietin-2 represents a candidate biomarker for the outcome of metastatic colorectal cancer patients treated with bevacizumab-containing therapy. Additionally, angiopoietin-2 has been associated with other diseases, including chronic kidney disease (51) and cerebral malaria (52).

In conclusion, the present study used a microarray platform to detect 1,000 proteins to identify dysregulated serum factors in RSA samples. This method was demonstrated to be effective in investigating dynamic alterations in protein profiles, and to select target proteins for further RSA research. The results indicated that IGFBP-rp1/IGFBP-7, Dkk3, RAGE and angiopoietin-2 expression were downregulated in RSA patients, suggesting that they may be important in the pathological process of RSA. Furthermore, upregulating them may inhibit the development of RSA. Therefore, these biomarkers represent potential predictive and diagnostic markers for RSA due to their high sensitivity and specificity. However, larger-scale studies are required to confirm the diagnostic value of these markers.

Acknowledgements

The present study was supported by Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding (China; grant no. ZYLX201510). The authors would like to thank Mr Xiangfu Ren (Beijing KeZhongZhi Biotechnology Co., Ltd., Beijing, China) for technical assistance and Ms. Hong Shao (Beijing KeZhongZhi Biotechnology Co., Ltd.) for valuable discussions.

Glossary

Abbreviations

RSA

recurrent spontaneous abortion

AUC

area under the curve

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