Maternal plasma microRNAs as potential biomarkers for triaging pregnancies of unknown location and ectopic pregnancy diagnosis

Christopher Kyriacou; Sung Hye Kim; Maria Arianoglou; Shabnam Bobdiwala; Margaret Pikovsky; Nina Parker; Jennifer Barcroft; Maya Al-Memar; Phillip R Bennett; David A MacIntyre; Tom Bourne; Vasso Terzidou

doi:10.1016/j.ncrna.2025.05.005

. 2025 May 8;13:162–173. doi: 10.1016/j.ncrna.2025.05.005

Maternal plasma microRNAs as potential biomarkers for triaging pregnancies of unknown location and ectopic pregnancy diagnosis

Christopher Kyriacou ^a,^b, Sung Hye Kim ^b,^c, Maria Arianoglou ^b,^c, Shabnam Bobdiwala ^a, Margaret Pikovsky ^a, Nina Parker ^a, Jennifer Barcroft ^a, Maya Al-Memar ^a, Phillip R Bennett ^a,^b,^c, David A MacIntyre ^a,^b,^c, Tom Bourne ^a,^b,^d, Vasso Terzidou ^b,^c,^e,^⁎

PMCID: PMC12156232 PMID: 40501483

Abstract

Background

Pregnancy of unknown location (PUL) is classified if an early pregnancy is not visualised on transvaginal ultrasonography (TVUS). Biomarkers currently used to triage PUL outcomes have varying accuracy. Delayed or missed diagnosis of ectopic pregnancies (EP) continue to cause significant morbidity and mortality. We investigated whether maternal plasma microRNAs (miRNAs) can predict and differentiate high-risk EP from viable (VIUP) or non-viable (NVIUP) intrauterine pregnancies.

Methods

Plasma was collected from women with PUL/EP (n = 120), mostly between four to eight weeks’ gestation, where outcomes of EP (n = 39), VIUP (n = 58) and NVIUP (miscarriage, n = 23) were determined using TVUS. Nanostring nCounter miRNA assay was used to examine the expression of ∼800 miRNAs in 22 women. Differentially expressed miRNAs were validated using RT-qPCR in 98 women.

Results

Nanostring nCounter miRNA assay identified 19 miRNAs which were expressed significantly higher in EP/NVIUP compared with VIUP. Two miRNAs were validated in a second, separate validation cohort using RT-qPCR: hsa-miR-21-5p in EP was 2.8-fold higher than in VIUP (p = 0.03, ROC AUC = 0.64), and hsa-miR-411-5p had 0.2-fold decreased expression (p = 0.02, ROC AUC = 0.66). Combining the divergent miRNAs as a ratio improved discrimination of EP from VIUP (p < 0.001, ROC AUC = 0.74).

Conclusion

Plasma miRNAs are differentially expressed in EP and VIUP and are detectable as early as four gestational weeks. Exploring miRNA targets may further understanding of EP pathophysiology, offering the potential to use miRNA as predictive and diagnostic markers in early pregnancy.

Keywords: MicroRNA, Biomarkers, PUL, Ectopic pregnancy, Plasma

1. Introduction

Pregnancy of unknown location (PUL) is classified in early pregnancy when a pregnancy is not visualised on transvaginal ultrasonography (TVUS) following a positive urine pregnancy test (UPT) [1]. PUL rates have been reported to vary between five to 42 % of all pregnancies [[2], [3], [4], [5]]. Management of PUL is based on the triage of these pregnancies to high versus low risk of clinical complications to decide community-based follow-up arrangements pending final diagnosis [1,6]. High-risk outcomes include ectopic pregnancies (EP – pregnancies partially or completely external to the endometrial cavity) or persistent PUL (PPUL – PUL with three or more serial beta-human chorionic gonadotrophin (hCG) values that vary by <15 %), and low-risk outcomes comprise intrauterine pregnancies (viable – VIUP, or non-viable – NVIUP/miscarriage) or failed PUL (FPUL – PUL with a negative UPT two weeks from initial assessment when presenting with a positive UPT) [1,6]. Ectopic pregnancies continue to carry significant risk of maternal morbidity and mortality if diagnosis is delayed or missed [7].

Whilst ectopic pregnancies, viable intrauterine pregnancies and miscarriages are diagnosed using TVUS, persistent PUL and failed PUL are more heterogeneous and require TVUS with the aid of serial hCG values (PPUL) or a negative urine pregnancy test (FPUL) [1]. It is likely that both PPUL and FPUL are very early intrauterine or ectopic pregnancies that are too small to confidently identify on TVUS [6].

Biomarkers are used for PUL triage to facilitate screening of cases as efficiently and as accurately as possible pending a final diagnosis. Biomarkers currently available in clinical practice include hCG (a marker of trophoblast function), and progesterone (a marker of ovarian corpus luteal function) [1,8,9]. These are used in isolation, as a ratio (utilising serial measurements over time), or within multinomial logistic regression models [1,[8], [9], [10], [11], [12], [13], [14], [15]]. The best available model (the M6) discriminates those at high-risk and low-risk of clinical complications well (area under the curve, AUC between 0.86–0.89), however, it has limited population generalisability [9]. Although novel marker candidates have been investigated, many are not appropriate or suitable for clinical use. Biomarkers used in isolation have poor sensitivity or specificity, with much of the evidence based on studies with small numbers, that have not been externally validated [9,[16], [17], [18]]. There is thus a clinical need to improve our ability to triage PUL pending diagnosis.

MicroRNAs (miRNAs) are single stranded, non-coding, 19–25 nucleotide molecules which regulate messenger RNA stability and transcription [19,20]. A proportion of miRNAs are shed into the circulation in a stable and quantifiable form [19,21,22]. They are important gene regulators, and their expression profiles change with pathophysiology [19,21]. They have been implicated in cardiac, pulmonary, neurological, endocrinological, haematological, rheumatological and oncological processes or pathologies [[23], [24], [25], [26], [27], [28], [29], [30], [31], [32]]. MiRNAs have been investigated in reproductive health and pregnancy outcomes. Outside of pregnancy, they are implicated in reproductive tract and fibroid development, as well as endometriosis [[32], [33], [34], [35], [36], [37]]. MiRNAs appear to also play a role in abnormal trophoblast invasion and choriocarcinoma [[38], [39], [40]]. Antenatally and intrapartum, miRNAs have been linked to mesenchymal hamartomas, neural tube defects, gestational diabetes, pre-eclampsia, fetal growth restriction, pre-term birth and uterine contractility [19,21,[41], [42], [43], [44], [45], [46], [47], [48], [49], [50]].

In early pregnancy, variable miRNA expression has been associated with blastocyst development, site and success of implantation, and thus miscarriage as well as EP [20,22,31,[51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61]]. MiRNAs have been suggested as novel biomarkers that could improve PUL outcome prediction beyond current clinical capabilities [53]. The objective of this study was to evaluate the potential of circulating miRNA markers in pregnancy of unknown location triage and ectopic pregnancy diagnosis.

2. Methods

2.1. Sex as a biological variable

Women alone were included in this work given the nature and relevance of the conditions under investigation.

2.2. Recruitment and sample collection

Women with a positive urine pregnancy test attending the early pregnancy assessment units (EPAU) at Imperial College Healthcare NHS Trust in London, UK, between July 2018 and December 2020 were recruited following classification of PUL or diagnosis of EP, mostly between four to eight weeks' gestation. TVUS ± PUL triage was performed as per local clinical guidelines until an outcome was confirmed, and ectopic pregnancies, miscarriages (NVIUP) and viable intrauterine pregnancies were included in the analysis. VIUP were confirmed at the pregnancy dating scan, usually at 12–14 weeks'gestation. FPUL and PPUL were excluded from analysis due to their heterogeneity (FPUL and PPUL cohorts are likely to contain both failing or persistent intrauterine or ectopic pregnancies that cannot be discerned using TVUS).

Whole blood samples were collected in EDTA tubes (K₂EDTA BD Vacutainer®), and plasma was isolated by centrifugation at 1962×g for 10 min at 4 °C (Megafuge 40R, Thermo Scientific, Waltham, USA). Isolated plasma was stored in 500 μL aliquots in RNase-free microtubes at −80 °C.

2.3. Study approval

The samples used in this project were collected following the approval of two studies by the Health Research Authority, the Health and Care Research Wales Research Ethics Committee, and either the North of Scotland Research Ethics Committee, or the London-Dulwich Research Ethics Committee. Their references are 14/NS/1078 (Assessment of Biomarkers in Pregnancy of Unknown Location and Ectopic pregnancy – ABPEP: ClinicalTrials.gov NCT04738370) and 19/LO/1154 (Assessment of Biomarkers in Ectopic Pregnancy – AMBER: ClinicalTrials.gov NCT04176549). Written informed consent was received prior to participation.

2.4. Data availability

Findings for this observational cohort study have been reported in accordance with the STROBE guidelines. Raw count data from the Nanostring nCounter miRNA profiling assay are available in Supplementary Table S1.

2.5. RNA extraction

RNA extraction was performed using Quick-cfRNA Serum & Plasma Kit (Zymo Research, Orange, USA – RRID:SCR_008968). Plasma samples were thawed on ice prior to centrifugation at 12,000×g for 15 min to remove any cell debris or precipitates as per manufacturer's instructions. The upper 200 μL of plasma was used for the RNA extraction to avoid cellular and/or platelet contamination. Samples were evaluated for haemolysis macroscopically, and via the use of spectrophotometric measurement of haemoglobin at 414 nm using Nanodrop 1000 (Thermo Scientific, Waltham, USA – RRID:SCR_016517). Samples with readings above 0.2 were deemed at risk of haemolysis.

In accordance with the manufacturer's protocol, the spike-in control cel-miR-254 (Qiagen, Hilden, Germany – RRID:SCR_008539) was added to each sample following lysis and denaturation of plasma with Quick-cfRNA Digestion Buffer to allow normalisation of any technical variation that may have occurred during RNA extraction.

2.6. nCounter microRNA assay and data analysis

The RNA samples for the discovery cohort were evaluated using nCounter cell-free miRNA profiling (Nanostring, Seattle, USA – RRID:SCR_021712), allowing direct fluorescence-based molecular barcoding of 798 individual miRNAs per sample without amplification. The nCounter Human v3 miRNA panel was used for each sample and it accounts for more than 95 % of all observed sequencing reads in miRbase version 21 (https://www.mirbase.org/), including all miRNAs denoted as ‘high confidence’ [62].

Counts of miRNAs were analysed using nSolver version 4.0 (Nanostring, Seattle, USA – RRID:SCR_003420) software, with normalisation to the 100 most expressed targets. Normalised miRNA counts were then compared between the outcome groups, EP, NVIUP, and VIUP. The miRNAs expressed below background level in more than 50 % of at least one outcome group were excluded from analysis (background level defined as two standard deviations above mean negative control counts). The raw count data is available in Supplementary Table S1.

2.7. Reverse transcription, real-time quantitative polymerase chain reaction (RT-qPCR) and data analysis

Reverse transcription and RT-qPCR was used for technical validation of the discovery work, as well as for the biological validation study which utilised a second, separate validation cohort. Following a literature review, three additional miRNA markers were also added to the biological validation work. Reverse transcription of the extracted RNA samples was achieved using the miRCURY LNA Universal^RT miRNA cDNA synthesis kits following the manufacturer's instructions (Qiagen). As part of the protocol, the spike-in control, UniSp6, was added (Qiagen) to allow normalisation of any variation during reverse transcription.

Endogenous miRNA controls were identified using NormFinder (Microsoft, Redmond, USA – RRID:SCR_003387) software. The most stable combination of miRNAs was chosen for the normalisation of RT-qPCR data [63]. The stability values created by NormFinder using miRNA counts profiled using nCounter and analysed using nSolver (Nanosting) reflect the intra- and inter-group variation in the expression of each individual and combination of miRNAs. The combination of two miRNAs with greatest stability were used. The NormFinder analysis is also available in Supplementary Table S1.

RT-qPCR was performed as per manufacturer's instructions using custom PCR panels with embedded LNA™ primers (Qiagen, Hilden, Germany – RRID:SCR_008539), and miRCURY LNA SYBR® Green master mix (Qiagen, Hilden, Germany – RRID:SCR_008539. RT-qPCR was performed in duplicate for the technical validation samples.

RT-qPCR reactions were carried out on the StepOnePlus™ Real-Time PCR system (Applied Biosystems, Waltham, USA – RRID:SCR_015805). The cycle conditions included initial PCR heat activation at 95 °C for 2 min, followed by 45 cycles of denaturation at 95 °C for 10 s and annealing/extension at 56 °C for 60 s. Melt curve analyses from 60 to 95 °C confirmed a single PCR product.

Raw fluorescence data were collected using the StepOne Software version 2.3 (Applied Biosystems, Waltham, USA – RRID:SCR_014281) for evaluation of Cq values and primer efficiencies using the LinRegPCR program version 2017.1 [64]. Following this, Cq data was processed by normalising to the inter-plate calibrator (UniSp3), UniSp6 reverse transcription control, cel-miR-254 RNA extraction control and the endogenous controls. Univariate analyses were then performed to compare miRNA fold changes between the different outcome groups. Any participant samples or miRNAs that did not consistently amplify were excluded. For the remainder, any normalised Cq values above 45 or below one were given a value of 45 (the maximum number of cycles in PCR reaction).

2.8. Statistics

Statistical software used included nSolver version 4.0 (Nanostring) and PRISM version 8.0 (GraphPad, San Diego, USA – RRID:SCR_002798). Demographics (age, ethnicity, gestational age to initial TVUS, and Body Mass Index – BMI) for all patient groups were tested for normality using the Shapiro-Wilk test with mean (standard deviation) and median (interquartile range) appropriately presented. One-way ANOVA/Kruskal-Wallis analyses were selected depending on data distribution, with categorical data analyses using Fisher's. nSolver (Nanostring) statistical analysis allowed comparison of log-transformed geometric means of expression counts assuming unequal variance between different outcome groups in the discovery work using an unpaired two-tailed t-test. MiRNAs with p < 0.05 and a false discovery rate (FDR) < 0.05 were considered to be differentially expressed between outcomes. Count data were plotted on the logarithmic scale using geometric mean and geometric standard deviation (SD), with statistically significant p values presented. For the technical and biological validation work, univariate analyses of the relative expression fold changes for each miRNA were compared between outcome groups. Data from all miRNA candidates in each outcome group were tested for normality using the Shapiro-Wilk test, and unpaired two-tailed t-test/Mann-Whitney U analysis was selected depending on data distribution. Fold change data were plotted on the logarithmic scale using geometric mean and geometric SD, with statistically significant p values presented. For ROC curves, area under the ROC curve (AUC) with 95 % confidence intervals (95 % CI) were also included. Significance was again determined by p value, with a level <0.05 deemed significant. Whilst no formal power calculation was performed due to the discovery nature of the study, the study population numbers were comparable to similar published work exploring miRNAs in fetal growth restriction and preterm birth [19,21].

3. Results

3.1. Clinical outcome and patient characteristics

A total of 290 women were recruited from EPAU and 120 women (41.4 %) with outcomes of either ectopic pregnancy, miscarriage (NVIUP) or viable intrauterine pregnancy were used for further analysis (Fig. 1). 170 women (58.6 %) were excluded from this study due to their outcome: 141 failed PUL, 9 persistent PUL, 8 IUP of uncertain viability and 12 lost to follow up (LTFU). For the discovery cohort and technical validation using RT-qPCR, 22 women (18.3 %) were selected for analysis, where 7 had EP, 7 NVIUP, and 8 VIUP. One ectopic pregnancy sample did not consistently amplify and was excluded from the technical validation. For the validation cohort, 98 women (81.7 %) were selected where 32 were EP, 16 NVIUP and 50 VIUP. One miscarriage sample subsequently did not consistently amplify and was excluded.

Fig. 1 — Flowchart of patient recruitment and breakdown of data available for discovery, technical and biological validation studies

Abbreviations: FPUL: Failed PUL; LTFU: Lost to follow up; PPUL: Persistent PUL; IPUV: Intrauterine pregnancy of unknown viability; PUL: Pregnancy of unknown location; EP: Ectopic pregnancy; NVIUP: Non-viable intrauterine pregnancy; VIUP: Viable intrauterine pregnancy.

The demographics for all three outcome groups for the separate discovery and biological validation cohorts were similar, except for the participants age in the biological validation cohort when comparing NVIUP with VIUP (p = 0.02, Kruskal-Wallis analysis), and when comparing ‘Black’ and ‘Other’ ethnicities within viable intrauterine and ectopic pregnancy cohorts (p = 0.04, Fisher's exact test) (Table 1, Table 2). The presence of ectopic pregnancy risk factors within the outcome groups of both the discovery and biological validation cohorts are described in Supplementary Tables S2 and S3, with the demographics and characteristics of each outcome group further divided by the ethnicity groups of Table 1, Table 2 in Supplementary Tables S4 and S5.

Table 1.

Demographics of discovery and technical validation cohort (N = 22) for microRNA analysis, divided by outcome.

Demographics		VIUP (N = 8)	EP (N = 7)	NVIUP (N = 7)	p-value
Mean age - years (SD)		32 (5.9)	35.3 (3.9)	34.4 (6.4)	0.60; 0.77
Individual p-values		0.60, 0.65	0.60, 0.77	0.65, 0.77	0.60; 0.77
Ethnicity N (%)	Caucasian	5 (62.5)	4 (57.1)	3 (42.9)	0.44; 0.99
	Asian	3 (37.5)	2 (28.6)	3 (42.9)
	Black	0 (0.0)	1 (14.3)	0 (0.0)
	Other	0 (0.0)	0 (0.0)	1 (14.3)
Individual p-value ranges		0.44–0.99	0.99	0.44–0.99
Mean gestational age - days (SD)		32.3 (4.8)	37.3 (12.9)	44.6 (11.8)	0.092; 0.36
Individual p-values		0.092, 0.36	0.36	0.092, 0.36	0.092; 0.36
Median BMI - kg/m² (IQR)		24.8 (21.5–31.3)	24.4 (21.9–31.2)	25.8 (22.2–32.0)^a	0.99
Individual p-values		0.99	0.99	0.99	0.99

Open in a new tab

p-value corresponds to one-way ANOVA (parametric, continuous), Kruskal-Wallis (non-parametric, continuous), or Fisher's (categorical) for the difference in study participants' demographics between outcome groups. For Fisher's, each outcome and ethnicity group were individually tested against one another. Individual p-values are specific to each outcome group under comparison, with overall p-value ranges listed in the end column.

Abbreviations: SD = Standard deviation; IQR = Interquartile range; VIUP = Viable intrauterine pregnancy; EP = Ectopic pregnancy; NVIUP = Non-viable intrauterine pregnancy; NS = Not significant; BMI = Body mass index.

BMI not available for 1 NVIUP.

Table 2.

Demographics of biological validation cohort (N = 97) for microRNA analysis, divided by outcome.

Demographics		VIUP (N = 50)	EP (N = 32)	NVIUP (N = 15)	p-value
Median age - years (IQR)		31 (25.8–35)	32 (27–37.5)	34 (31–42)^a	0.018^a; 0.68
Individual p-values		0.018^a, 0.68	0.26, 0.68	0.018^a, 0.26	0.018^a; 0.68
Ethnicity N (%)	Caucasian	28 (56.0)	16 (50.0)	7 (46.7)	0.041^b; 0.99
	Asian	7 (14.0)	6 (18.8)	3 (20.0)
	Black	8 (16.0)^b	1 (3.1)^b	3 (20.0)
	Other	7 (14.0)^b	9 (28.1)^b	2 (13.3)
Individual p-value ranges		0.041^b–0.99	0.041^b–0.99	0.077–0.99
Median gestational age - days (IQR)		33 (31.0–38.5)^c	42.5 (27.5–48.8)^d	40 (34.0–44.0)	0.18; 0.99
Individual p-values		0.18, 0.47	0.18, 0.99	0.47, 0.99	0.18; 0.99
Median BMI - kg/m² (IQR)		26.4 (22.8–28.8)^e	23.7 (20.6–26.7)^f	24 (20.3–28.3)	0.46; 0.99
Individual p-values		0.46, 0.60	0.46, 0.99	0.60, 0.99	0.46; 0.99

Open in a new tab

Abbreviations: IQR = Interquartile range; VIUP = Viable intrauterine pregnancy; EP = Ectopic pregnancy; NVIUP = Non-viable intrauterine pregnancy; NS = Not significant; BMI = Body mass index.

Significant difference comparing NVIUP with VIUP.

Significant difference comparing ‘Black’ and ‘Other’ ethnicities within VIUP and EP cohorts.

Gestational age not available for 4 VIUP.

Gestational age not available for 4 EP.

BMI not available for 1 VIUP.

BMI not available for 1 EP.

3.2. Plasma miRNA expression profiling using nCounter assay

A total of 62 miRNAs were found to be expressed above the background in more than 50 % of samples in any one of the outcome groups (Table S6). The expression of these miRNAs were compared between outcome groups using nSolver (Nanostring) with 19 miRNAs identified to be significantly different between control (VIUP) and ectopic pregnancy ± miscarriage (NVIUP) outcome groups (Table 3, Table 4, Table 5). There were eight miRNAs expressed significantly higher only in EP compared to VIUP (Table 3, Fig. 2), seven only in NVIUP when compared to VIUP (Table 4, Fig. 3), and four miRNAs which were increased in both EP and NVIUP when compared to VIUP (Table 5, Fig. 4).

Table 3.

Discovery microRNA markers meeting significance when comparing viable intrauterine pregnancy with ectopic pregnancy (N = 15) using nCounter.

MicroRNA	VIUP (N = 8) vs EP (N = 7) Ratio (95 % CI)	Relative change	p-value
hsa-miR-222-3p	4.5∗ (1.7–12.1)	EP > VIUP	0.0060
hsa-miR-20a-5p + hsa-miR-20b-5p^a	6.0∗ (1.4–25.0)	EP > VIUP	0.019
hsa-miR-21-5p	5.4∗ (1.3–23.0)	EP > VIUP	0.027
hsa-miR-185-5p	4.8∗ (1.2–18.8)	EP > VIUP	0.028
hsa-miR-148a-3p	5.1∗ (1.1–23.2)	EP > VIUP	0.036
hsa-miR-19b-3p	4.5∗ (1.1–18.8)	EP > VIUP	0.038
hsa-miR-1285-5p	4.4∗ (1.1–18.1)	EP > VIUP	0.038
hsa-miR-22-3p	4.6∗ (1.0–20.5)	EP > VIUP	0.044

Open in a new tab

p-value corresponds to unpaired two-tailed t-test, for the difference between outcome groups.

Ordered by p-value; ∗Designates ratio significance.

Abbreviations: 95 % CI = 95 % confidence interval; VIUP = Viable intrauterine pregnancy; EP = Ectopic pregnancy.

Nanostring assay ran both these targets together.

Table 4.

Discovery microRNA markers meeting significance when comparing viable intrauterine pregnancy with non-viable intrauterine pregnancy (N = 15) using nCounter.

MicroRNA	VIUP (N = 8) vs NVIUP (N = 7) Ratio (95 % CI)	Relative change	p-value
has-miR-107	5.3∗ (1.8–15.2)	NVIUP > VIUP	0.0047
hsa-miR-223-3p	2.0∗ (1.1–3.7)	NVIUP > VIUP	0.028
hsa-let-7d-5p	3.9∗ (1.1–13.9)	NVIUP > VIUP	0.035
hsa-miR-191-5p	2.5∗ (1.1–5.8)	NVIUP > VIUP	0.037
hsa-miR-29b-3p	4.5∗ (1.0–19.9)	NVIUP > VIUP	0.045
hsa-miR-374a-5p	1.6∗ (1.0–2.5)	NVIUP > VIUP	0.046
hsa-miR-199a-3p + hsa-miR-199b-3p^a	3.7∗ (1.0–13.2)	NVIUP > VIUP	0.047

Open in a new tab

p-value corresponds to unpaired two-tailed t-test, for the difference between outcome groups.

Ordered by p-value; ∗Designates ratio significance.

Abbreviations: 95 % CI = 95 % confidence interval; VIUP = Viable intrauterine pregnancy; NVIUP = Non-viable intrauterine pregnancy.

Nanostring assay ran both these targets together.

Table 5.

Discovery microRNA markers meeting significance when comparing viable intrauterine pregnancy with both ectopic pregnancy and non-viable intrauterine pregnancy (N = 22) using nCounter.

MicroRNA	VIUP (N = 8) vs EP (N = 7) Ratio (95 % CI)	VIUP (N = 8) vs NVIUP (N = 7) Ratio (95 % CI)	Relative change	p-value
hsa-miR-1246	13.9∗ (2.8–70.3)	6.0∗ (1.2–29.0)	EP > VIUP	0.0062
hsa-miR-1246	13.9∗ (2.8–70.3)	6.0∗ (1.2–29.0)	NVIUP > VIUP	0.030
hsa-miR-130a-3p	5.7∗ (1.3–26.0)	8.2∗ (1.9–35.2)	EP > VIUP	0.026
hsa-miR-130a-3p	5.7∗ (1.3–26.0)	8.2∗ (1.9–35.2)	NVIUP > VIUP	0.0090
hsa-miR-320e	5.3∗ (1.4–20.6)	3.9∗ (1.3–11.2)	EP > VIUP	0.020
hsa-miR-320e	5.3∗ (1.4–20.6)	3.9∗ (1.3–11.2)	NVIUP > VIUP	0.017
hsa-miR-30d-5p	3.0∗ (1.2–7.3)	3.3∗ (1.2–9.1)	EP > VIUP	0.022
hsa-miR-30d-5p	3.0∗ (1.2–7.3)	3.3∗ (1.2–9.1)	NVIUP > VIUP	0.023

Open in a new tab

p-value corresponds to unpaired two-tailed t-test, for the difference between outcome groups.

Ordered by p-value of EP/NVIUP vs VIUP for each microRNA; ∗Designates ratio significance.

Abbreviations: 95 % CI = 95 % confidence interval; VIUP = Viable intrauterine pregnancy; EP = Ectopic pregnancy; NVIUP = Non-viable intrauterine pregnancy.

Fig. 2 — Eight discriminatory miRNAs in early pregnancy plasma when comparing VIUP with EP (N = 15) in discovery study. nCounter probe counts for A: hsa-miR-222-3p, B: hsa-miR-20a-5p + hsa-miR-20b-5p, C: hsa-miR-21-5p, D: hsa-miR-185-5p, E: hsa-miR-148a-3p, F: hsa-miR-19b-3p, G: hsa-miR-1285-5p, and H: hsa-miR-22-3p detected in human plasma in women in early pregnancy who subsequently had a confirmed VIUP (N = 8, green), EP (N = 7, red), or NVIUP (N = 7, purple)

∗Statistical significance of differences between groups was tested using Nanostring nSolver platform. Graphs show geometric mean with geometric standard deviation and p-values, with outcome on x axis, and log(10) counts on y axis. Only statistically significant differences are presented.

Abbreviations: VIUP = Viable intrauterine pregnancy; EP = Ectopic pregnancy; NVIUP = Non-viable intrauterine pregnancy.

Fig. 3 — Seven discriminatory miRNAs in early pregnancy plasma when comparing VIUP with NVIUP (N = 15) in discovery study. nCounter probe counts for A: hsa-miR-107, B: hsa-miR-223-3p, C: hsa-let-7d-5p, D: hsa-miR-191-5p, E: hsa-miR-29b-3p, F: hsa-miR-374a-5p, and G: hsa-miR-199a-3p + hsa-miR-199b-3p detected in human plasma in women in early pregnancy who subsequently had a confirmed VIUP (N = 8, green), EP (N = 7, red), or NVIUP (N = 7, purple)

∗Statistical significance of differences between groups was tested using Nanostring nSolver platform. Graphs show geometric mean with geometric standard deviation and p-values, with outcome on x axis, and log(10) counts on y axis. Only statistically significant differences are presented.

Abbreviations: VIUP = Viable intrauterine pregnancy; EP = Ectopic pregnancy; NVIUP = Non-viable intrauterine pregnancy.

Fig. 4 — Four discriminatory miRNAs in early pregnancy plasma when comparing VIUP with EP and NVIUP (N = 22) in discovery study. nCounter probe counts for A: hsa-miR-1246, B: hsa-miR-130a-3p, C: hsa-miR-320e, and D: hsa-miR-30d-5p detected in human plasma in women in early pregnancy who subsequently had a confirmed VIUP (N = 8, green), EP (N = 7, red), or NVIUP (N = 7, purple)

∗Statistical significance of differences between groups was tested using Nanostring nSolver platform. Graphs show geometric mean with geometric standard deviation and p-values, with outcome on x axis, and log(10) counts on y axis. Statistically significant differences are presented.

Abbreviations: VIUP = Viable intrauterine pregnancy; EP = Ectopic pregnancy; NVIUP = Non-viable intrauterine pregnancy.

3.3. Technical validation of nCounter microRNA profiling using RT-qPCR

We performed technical validation of the 19 miRNAs identified using nCounter (Nanostring). Two miRNAs, hsa-miR-126-3p and hsa-let-7g-5p were identified to have the highest inter- and intra-group stability using NormFinder and were used as endogenous controls [63]. Univariate analysis of the DeltaCq values for each miRNA candidate are shown in Table 3. From the 19 miRNAs examined, one miRNA (hsa-miR-21-5p) showed significant increase in EP compared to VIUP (Table 6), and two miRNAs (hsa-miR-130a-3p and hsa-miR-374a-5p) were found to be significantly increased in NVIUP compared to VIUP (Table 7, Table 8).

Table 6.

Technical validation of microRNA markers comparing viable intrauterine pregnancy with ectopic pregnancy (N = 14) using reverse transcription quantitative polymerase chain reaction (RT-qPCR).

MicroRNA	VIUP (N = 8)	EP (N = 6)	VIUP vs EP Fold change	Relative change	p-value
has-miR-21-5p	1.0 (0.28)	3.2 (2.75)	3.2∗	EP > VIUP	0.0013
hsa-miR-222-3p	1.0 (0.79)	1.8 (1.01)	1.8	EP > VIUP	0.12
hsa-miR-148a-3p	1.0 (0.47)	2.2 (2.05)	2.2	EP > VIUP	0.13
hsa-miR-185-5p	1.0 (0.28)	1.2 (0.57)	1.2	EP > VIUP	0.45
hsa-miR-20a-5p	1.0 (0.43)	1.2 (0.52)	1.2	EP > VIUP	0.52
hsa-miR-19b-3p	1.0 (0.48)	1.2 (0.81)	1.2	EP > VIUP	0.54
hsa-miR-1285-5p	1.0 (0.37)	0.9 (0.45)	0.9	EP < VIUP	0.57
hsa-miR-20b-5p	1.0 (0.32)	1.1 (0.58)	1.1	EP > VIUP	0.79
hsa-miR-22-3p	1.0 (0.45)	1.2 (0.89)	1.2	EP > VIUP	0.85

Open in a new tab

Mean DeltaCq (SD) presented in outcome columns. p-value corresponds to unpaired two-tailed t-test (parametric, continuous), or Mann-Whitney U (non-parametric, continuous), for the difference in fold change between outcome groups.

Ordered by p-value; ∗Designates relative expression fold change significance.

Abbreviations: VIUP = Viable intrauterine pregnancy; EP = Ectopic pregnancy; SD = Standard deviation.

Table 7.

Technical validation of microRNA markers comparing viable intrauterine pregnancy with non-viable intrauterine pregnancy (N = 15) using reverse transcription quantitative polymerase chain reaction (RT-qPCR).

MicroRNA	VIUP (N = 8)	NVIUP (N = 7)	VIUP vs NVIUP Fold change	Relative change	p-value
hsa-miR-374a-5p	1.0 (0.14)	2.0 (1.20)	2.0∗	NVIUP > VIUP	0.0022
hsa-miR-199a-3p	1.0 (0.87)	2.0 (1.34)	2.0	NVIUP > VIUP	0.054
hsa-miR-107	1.0 (0.32)	1.5 (0.52)	1.5	NVIUP > VIUP	0.058
hsa-miR-223-3p	1.0 (0.77)	1.6 (1.09)	1.6	NVIUP > VIUP	0.072
hsa-miR-29b-3p	1.0 (0.35)	1.2 (0.22)	1.2	NVIUP > VIUP	0.19
hsa-miR-191-5p	1.0 (0.25)	1.3 (0.62)	1.3	NVIUP > VIUP	0.28
hsa-let-7d-5p	1.0 (0.60)	0.9 (0.36)	0.9	NVIUP < VIUP	0.70

Open in a new tab

Ordered by p-value; ∗Designates relative expression fold change significance.

Abbreviations: VIUP = Viable intrauterine pregnancy; NVIUP = Non-viable intrauterine pregnancy; SD = Standard deviation.

Table 8.

Technical validation of microRNA markers comparing viable intrauterine pregnancy with both ectopic pregnancy and non-viable intrauterine pregnancy (N = 21) using reverse transcription quantitative polymerase chain reaction (RT-qPCR).

MicroRNA	VIUP (N = 8)	EP (N = 6)	NVIUP (N = 7)	VIUP vs EP Fold change	VIUP vs NVIUP Fold change	Relative change	p-value
hsa-miR-130a-3p	1.0 (0.61)	1.3 (0.57)	2.2 (1.61)	1.3	2.2∗	EP > VIUP	0.14
hsa-miR-130a-3p	1.0 (0.61)	1.3 (0.57)	2.2 (1.61)	1.3	2.2∗	NVIUP > VIUP	0.014
hsa-miR-30d-5p	1.0 (0.38)	1.7 (1.01)	1.5 (0.68)	1.7	1.5	EP > VIUP	0.11
hsa-miR-30d-5p	1.0 (0.38)	1.7 (1.01)	1.5 (0.68)	1.7	1.5	NVIUP > VIUP	0.094
hsa-miR-1246	1.0 (0.63)	1.3 (1.36)	0.5 (0.29)	1.3	0.5	EP > VIUP	0.95
hsa-miR-1246	1.0 (0.63)	1.3 (1.36)	0.5 (0.29)	1.3	0.5	NVIUP < VIUP	0.10
hsa-miR-320e	1.0 (0.62)	1.8 (1.43)	1.4 (1.20)	1.8	1.4	EP > VIUP	0.41
hsa-miR-320e	1.0 (0.62)	1.8 (1.43)	1.4 (1.20)	1.8	1.4	NVIUP > VIUP	0.96

Open in a new tab

Ordered by p-value of EP/NVIUP vs VIUP for each microRNA; ∗Designates relative expression fold change significance.

Abbreviations: VIUP = Viable intrauterine pregnancy; EP = Ectopic pregnancy; NVIUP = Non-viable intrauterine pregnancy; SD = Standard deviation.

3.4. Biological validation of microRNA candidates using RT-qPCR

Biological validation was performed for the three technically validated miRNAs (hsa-miR-21-5p, hsa-miR-130a-3p and hsa-miR-374a-5p) in a separate patient population utilised as a second validation cohort (Fig. 1, n = 98). Three additional miRNA markers were also added following literature review: hsa-miR-411-5p, hsa-miR-324-3p and hsa-miR-873-3p [52,54,57,59]. These three additional miRNAs were reported to be expressed at lower levels in ectopic pregnancy when compared to viable intrauterine pregnancy (or termination of pregnancy patients). Whilst this trend was confirmed by the findings of the nCounter assay (Table S1), where the differences in Nanostring counts between EP and VIUP were either significant (hsa-miR-411-5p, p = 0.040) or borderline (hsa-miR-324-3p, p = 0.090; hsa-miR-873-3, p = 0.069), they were excluded from further discovery cohort analysis due to their low levels of expression (below background level in more than 50 % of at least one outcome group). However, in this separate biological validation, we aimed to exploit the inverse expression relationship between these miRNAs and the increasing miRNAs identified in the discovery cohort, to improve the discrimination of ectopic pregnancy.

Two of the six miRNAs examined (hsa-miR-21-5p and hsa-miR-411-5p) were biologically validated (Table 9). Hsa-miR-21-5p was significantly increased in EP compared to VIUP (p = 0.035) with a mean fold change of 2.8 (SD 5.46), and hsa-miR-411-5p was decreased in EP (p = 0.016) with a mean fold change of 0.2 (SD 0.20) (Table 9, Fig. 5). Hsa-miR-130a-3p, hsa-miR-374a-5p and hsa-miR-324-3p did not show any significant differences between outcome groups (Table 9, Table 10, Fig. S1), and hsa-miR-873-3p failed to amplify during RT-qPCR. Six of the 485 normalised Cq values obtained from the five miRNAs that did amplify were given a value of 45 during the analysis (1.2 %).

Table 9.

Biological validation of microRNA markers comparing viable intrauterine pregnancy with ectopic pregnancy (N = 82) using reverse transcription quantitative polymerase chain reaction (RT-qPCR).

MicroRNA	VIUP (N = 50)	EP (N = 32)	VIUP vs EP Fold change	Relative change	p-value
hsa-miR-411-5p	1.0 (3.65)	0.2 (0.20)	0.2∗	EP < VIUP	0.016
hsa-miR-21-5p	1.0 (0.44)	2.8 (5.46)	2.8∗	EP > VIUP	0.035
hsa-miR-324-3p	1.0 (0.83)	1.0 (0.74)	1.0	EP = VIUP	0.62

Open in a new tab

Mean DeltaCq (SD) presented in outcome columns. p-value corresponds to Mann-Whitney U (non-parametric, continuous). For the difference in fold change between outcome groups.

Ordered by p-value; ∗Designates relative expression fold change significance.

Abbreviations: VIUP = Viable intrauterine pregnancy; EP = Ectopic pregnancy; SD = Standard deviation.

Fig. 5 — Two discriminatory miRNAs in early pregnancy plasma when comparing VIUP with EP (N = 82) in biological validation study. Fold changes for A: hsa-miR-21-5p, and B: hsa-miR-411-5p in women in early pregnancy who subsequently had a confirmed VIUP (N = 50, green), or EP (N = 32, red)

∗Statistical significance of differences between groups was tested using Mann-Whitney (non-parametric, continuous). Graphs show geometric mean with geometric standard deviation and p-values, with outcome on x axis, and log(10) fold change on y axis. Statistically significant differences are presented.

Abbreviations: VIUP = Viable intrauterine pregnancy; EP = Ectopic pregnancy.

Table 10.

Biological validation of microRNA markers comparing viable intrauterine pregnancy with non-viable intrauterine pregnancy (N = 65) using reverse transcription quantitative polymerase chain reaction (RT-qPCR).

MicroRNA	VIUP (N = 50)	NVIUP (N = 15)	VIUP vs NVIUP Fold change	Relative change	p-value
hsa-miR-374a-5p	1.0 (0.42)	0.9 (0.51)	0.9	NVIUP < VIUP	0.26
hsa-miR-130a-3p	1.0 (0.60)	0.9 (0.39)	0.9	NVIUP < VIUP	0.68

Open in a new tab

Mean DeltaCq (SD) presented in outcome columns. P-value corresponds to unpaired two-tailed t-test (parametric, continuous), or Mann-Whitney U (non-parametric, continuous), for the difference in fold change between outcome groups.

Ordered by p-value; Relative expression fold change presented.

Abbreviations: VIUP = Viable intrauterine pregnancy; NVIUP = Non-viable intrauterine pregnancy; SD = Standard deviation.

3.5. Assessment of the predictive ability and diagnostic test performance of identified miRNA markers

The predictive ability of the two biologically validated miRNA markers for ectopic pregnancy were assessed using ROC curve analyses (Fig. 6). As individual markers, hsa-miR-21-5p and hsa-miR-411-5p showed ROC AUC of 0.64 (p = 0.035) and 0.66 (p = 0.017), respectively. To explore the two markers in combination for the prediction of EP, the inverse correlation of hsa-miR-21-5p and hsa-miR-411-5p expression was used to create a ratio (Fig. 7). The ratio of hsa-miR-21-5p/hsa-miR-411-5p showed significant difference between EP and viable intrauterine pregnancies (p = 0.0003), and the ratio revealed improved predictive ability with ROC AUC of 0.74 (p = 0.0004).

Fig. 7 — Two discriminatory miRNAs in early pregnancy plasma when comparing VIUP with EP (N = 82) in biological validation study. A: Fold changes, and B: area under the Receiver operating characteristic (ROC) curve (AUC) for hsa-miR-21-5p/hsa-miR-411-5p ratio, in women in early pregnancy who subsequently had a confirmed VIUP (N = 50, green), or EP (N = 32, red)

∗Statistical significance of differences between groups was tested using Mann-Whitney (non-parametric, continuous). Graph shows geometric mean with geometric standard deviation and p-values, with outcome on x axis, and log(10) fold change on y axis.

ROC curve with area, 95 % confidence interval and p-value presented.

Statistically significant differences are presented.

Abbreviations: VIUP = Viable intrauterine pregnancy; EP = Ectopic pregnancy; ROC = Receiver operating characteristic.

3.6. Target gene and pathway enrichment analysis

An in-silico analysis of the genes and pathways regulated by hsa-miR-21-5p and hsa-miR-411-5p was undertaken to further understand their potential role in ectopic pregnancy pathophysiology. The miRDIP miRNA data integration portal (University Health Network, Toronto, Canada – RRID:SCR_016770) identified 413 genes with binding sites for these miRNA markers [20,[65], [66], [67]]. Pathway analysis was then performed using the Reactome (Reactome project, Ontario/New York/Hinxton/Oregon, Canada/USA/UK – RRID:SCR_003485) platform [[68], [69], [70], [71], [72], [73]].

Of the 1129 pathways identified using the Reactome platform, 162 met significance (defined as p ≤ 0.05) and are presented in Table S7. These include signal transduction pathways via growth factor receptors, transcription regulation pathways, oestrogen dependent gene expression, and cytokine signalling, particularly interleukins (IL-4, IL-6, IL-12, and IL-13). IL-6, a proinflammatory interleukin, has been associated with ovarian hormonal stimulation, follicle genesis, and implantation, with higher levels identified in the fallopian tubes of women with EP [53]. Pilot work previously presented suggested a difference in both IL-6 and oestradiol expression in EP [74,75].

4. Discussion

There is an ongoing clinical need to improve pregnancy of unknown location triage to reduce potential life-threatening consequences of missed or delayed ectopic pregnancy diagnoses. In this study, we have explored miRNAs as potential novel markers that may play a role in improving PUL triage pending diagnosis using transvaginal ultrasonography. We report the identification of 19 miRNAs differentially expressed in the three outcome groups (VIUP, NVIUP and EP) through maternal plasma miRNA profiling in our discovery work. Three of these were technically validated using RT-qPCR, and two miRNA markers, hsa-miR-21-5p and hsa-miR-411-5p, were further validated in a second, separate validation cohort. We found both validated miRNAs were able to differentiate viable intrauterine pregnancies from ectopic pregnancies, with hsa-miR-21-5p increasing and the hsa-miR-411-5p decreasing significantly in EP. The combination of their relative expression as a ratio demonstrated good predictive ability for EP with ROC AUC of 0.74, indicating their potential use in EP prediction as early as four weeks’ gestation.

Several putative miRNAs biomarkers of ectopic pregnancy have been previously reported (Table S8) [52,54,[56], [57], [58], [59],61,76,77]. Dysregulation of these miRNAs have been hypothesized to affect endometrial receptivity, production of reproductive regulators such as kisspeptin, influence implantation site (with associated tissue trauma and inflammation), vascularity and trophoblast formation [53,54,59,78,79]. Whilst there were no peer-reviewed articles that linked hsa-miR-21-5p to EP, there was one paper that discussed hsa-miR-411-5p in this manner [54].

There are key differences between the literature available and our work: a) none of the studies had focussed on a PUL population, b) many used tissue, or serum samples, c) most had low study numbers, and d) the majority of EP included had later gestational ages. We have examined a unique population in this context with early miRNA detection suggesting the ability of prompt EP prediction and detection (with consequent early intervention), which could reduce morbidity. Plasma collection from women with PUL is a non-invasive (unlike tissue), easily tolerated and well accepted blood test, which unlike serum is not affected by white blood cells or platelets where miRNAs are released during clotting. Yi Feng et al. has previously examined tissue samples from women undergoing termination of pregnancy and compared with fallopian tube samples (invasive) from women undergoing salpingectomy for tubal EP (not PUL) [54]. Amongst the miRNAs identified, lower expression of hsa-miR-411-5p was noted in 20 EP with an average gestational age of nine weeks.

Following our in-silico analyses, a separate literature review of hsa-miR-21-5p confirmed its involvement in the pathophysiology of multiple pregnancy complications. These include dysregulation of endometrial receptivity, proliferation, apoptosis and migration via the PDCD4/AKT, PI3K/AKY (FOXO3) and JAK/STAT3 pathways, hypoxia-related changes, increased postpartum cardiovascular risk in women previously affected by gestational diabetes, success of in-vitro fertilisation and ovarian reserve, placentation, gestational hypertension, pre-eclampsia and intrahepatic cholestasis of pregnancy, preterm premature rupture of membranes and pre term birth, placental and fetal growth [[80], [81], [82], [83], [84], [85], [86], [87], [88], [89], [90], [91], [92], [93], [94]].

PTEN (phosphates and tensin homologue deleted on chromosome ten) is quoted as one of the most significant pathways within the in-silico analysis. However, PTEN is also linked to hsa-miR-411-5p expression [95]. Whilst PTEN overexpression negatively affects cell survival, hsa-miR-411-5p causes downregulation, allowing trophoblast proliferation, migration, and invasion [95]. Nuclear paraspeckle assembly transcript 1 (NEAT1) inhibits hsa-miR-411-5p expression, upregulating PTEN which in turn promotes trophoblast apoptosis and pre-eclampsia [95].

Whilst these findings support both hsa-miR-21-5p and hsa-miR-411-5p potentially contributing to ectopic pregnancy aetiology in a biologically plausible manner, gaining a functional understanding may prove beneficial when considering their use in combination with other epigenetic markers, proteins or hormones not yet identified to further improve EP prediction, diagnosis, and subsequent management. Given the complexity of early pregnancy processes and pathophysiology, opportunities to use various statistical or algorithmic approaches beyond the scope of this paper may also subsequently arise.

Limitations of the study pertain to the overall numbers, the specific demographics, and the subjective assessment of ultrasound, which all could potentially confound results as well as limit findings. However, whilst the proportions of each outcome group were not reflective of population prevalence, and patients were recruited following either PUL classification or EP diagnosis, the enriched EP population, including ectopic pregnancies at diagnosis, allowed this relatively uncommon early pregnancy outcome to be evaluated with greater statistical power, with total population numbers comparable to similar published work, and matched demographically as closely as the population allowed [19,21]. The results, from a Health Policy perspective, lay important groundwork for ongoing research into how miRNA can contribute to improving day to day clinical workflow and patient safety in early pregnancy, both of which would likely also carry economic benefits. Unfortunately, from a total of 290 women, 170 were excluded due to their outcome diagnosis of failed or persistent PUL, both heterogenic groups where pregnancy location could not be confidently differentiated, as well as those lost to follow up or with an intrauterine pregnancy of uncertain viability. It is also likely that miRNAs differentiating VIUP with miscarriages (NVIUP) did not validate due to the heterogeneity of the NVIUP population following a PUL classification. For example, intrauterine pregnancies of unknown viability (IPUV) may have miscarried in the first trimester with or without the development of fetal cardiac activity.

Validation in a larger population, the inclusion of all pregnancy of unknown location outcomes, and a sensitivity analysis are the next steps in rigorously evaluating the potential clinical usefulness of hsa-miR-21-5p and hsa-miR-411-5p, both alone and as part of ratio. To facilitate such a large deployment of these miRNAs will require the digital signal of these candidate biomarkers to be merged with the digital signals of two or more independent modalities. This fusion of technology would increase generalisability and access to the markers without compromising workflow efficiency and precision, allowing outpatient early pregnancy use in a manner that is rapid but accurate.

We have demonstrated the potential of circulating miRNA markers for PUL triage by predicting EP in early pregnancy, which can be measured via the gold-standard RT-qPCR method that is an efficient, cost-effective, and readily available technology for translational use in clinical practice. With better diagnostics, the potential for further advancements in ectopic pregnancy therapeutics is also high, using proposed vehicles such as nanoparticles for targeted interventions in the management of pregnancy complications [96].

CRediT authorship contribution statement

Christopher Kyriacou: Writing – review & editing, Writing – original draft, Validation, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. Sung Hye Kim: Writing – review & editing, Validation, Methodology, Formal analysis, Conceptualization. Maria Arianoglou: Writing – review & editing, Resources, Methodology. Shabnam Bobdiwala: Writing – review & editing, Data curation. Margaret Pikovsky: Writing – review & editing, Data curation. Nina Parker: Writing – review & editing, Data curation. Jennifer Barcroft: Writing – review & editing, Data curation. Maya Al-Memar: Writing – review & editing, Data curation. Phillip R. Bennett: Writing – review & editing, Visualization, Supervision. David A. MacIntyre: Writing – review & editing, Visualization. Tom Bourne: Writing – review & editing, Visualization, Supervision, Funding acquisition, Conceptualization. Vasso Terzidou: Writing – review & editing, Visualization, Supervision, Project administration, Methodology, Investigation, Formal analysis, Conceptualization.

Trial registration

ClinicalTrials.gov NCT04738370, NCT04176549.

Funding

Imperial Health Charity; March of Dimes; National Institute for Health Research Imperial Biomedical Research Centre.

Declaration of competing interest

The findings of this study have been filed and published under the remit of two patent applications: 11,075 (microRNA discovery work – GB 2113344·2) and 10,439 (microRNA biological validation work – PCT/GB2020/050,324). The authors have declared that no other conflict of interest exists.

Acknowledgements and Funding

We acknowledge the support staff of the Early Pregnancy Assessment Unit, the women's health research centre, and the Institute of Reproductive and Development Biology who aided in the creation of this work. CK is supported by the Imperial Health Charity, grant number RFPrD1920/116. SB was supported by NIHR CLAHRC NWL, grant number RDIP0033. NP is supported by Tommy's National Centre for Miscarriage Research, Imperial College London. JB is supported by the Imperial Health Charity, grant number RFPR2122_13. MAM, PB, DM, TB and VT are supported by the NIHR Imperial Biomedical Research Centre, grant number IS-BRC-121VT5-20013. SK, MA, PB, DM and VT are supported by March of Dimes. The views expressed are those of the author(s) and not necessarily those of the National Health Service, the National Institute of Health Research, or the Department of Health. The funders had no role in study design, data collection, analysis, interpretation and reporting, or decision to publish.

Footnotes

Peer review under the responsibility of Editorial Board of Non-coding RNA Research.

^{Appendix A}

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ncrna.2025.05.005.

Appendix A. Supplementary data

The following are the Supplementary data to this article.

Multimedia component 1

mmc1.docx^{(161.1KB, docx)}

Multimedia component 2

mmc2.xlsx^{(329.4KB, xlsx)}

Multimedia component 3

mmc3.doc^{(90.5KB, doc)}

References

1.Bobdiwala S., Al-Memar M., Farren J., Bourne T. Factors to consider in pregnancy of unknown location. Womens Health (Lond). 2017;13(2):27–33. doi: 10.1177/1745505717709677. [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Banerjee S., Aslam N., Woelfer B., Lawrence A., Elson J., Jurkovic D. Expectant management of early pregnancies of unknown location: a prospective evaluation of methods to predict spontaneous resolution of pregnancy. BJOG. 2001;108(2):158–163. doi: 10.1111/j.1471-0528.2001.00031.x. [DOI] [PubMed] [Google Scholar]
3.Mol B.W., Hajenius P.J., Engelsbel S., Ankum W.M., Van der Veen F., Hemrika D.J., et al. Serum human chorionic gonadotropin measurement in the diagnosis of ectopic pregnancy when transvaginal sonography is inconclusive. Fertil. Steril. 1998;70(5):972–981. doi: 10.1016/s0015-0282(98)00278-7. [DOI] [PubMed] [Google Scholar]
4.Kirk E., Condous G., Van Calster B., Van Huffel S., Timmerman D., Bourne T. Rationalizing the follow-up of pregnancies of unknown location. Hum. Reprod. 2007;22(6):1744–1750. doi: 10.1093/humrep/dem073. [DOI] [PubMed] [Google Scholar]
5.Cordina M., Schramm-Gajraj K., Ross J.A., Lautman K., Jurkovic D. Introduction of a single visit protocol in the management of selected patients with pregnancy of unknown location: a prospective study. BJOG. 2011;118(6):693–697. doi: 10.1111/j.1471-0528.2011.02893.x. [DOI] [PubMed] [Google Scholar]
6.Kirk E., Bottomley C., Bourne T. Diagnosing ectopic pregnancy and current concepts in the management of pregnancy of unknown location. Hum. Reprod. Update. 2014;20(2):250–261. doi: 10.1093/humupd/dmt047. [DOI] [PubMed] [Google Scholar]
7.Webster K., Eadon H., Fishburn S., Kumar G., Committee G. Ectopic pregnancy and miscarriage: diagnosis and initial management: summary of updated NICE guidance. Br. Med. J. 2019;367 doi: 10.1136/bmj.l6283. [DOI] [PubMed] [Google Scholar]
8.Bobdiwala S., Saso S., Verbakel J.Y., Al-Memar M., Van Calster B., Timmerman D., et al. Diagnostic protocols for the management of pregnancy of unknown location: a systematic review and meta-analysis. BJOG. 2019;126(2):190–198. doi: 10.1111/1471-0528.15442. [DOI] [PubMed] [Google Scholar]
9.Christodoulou E., Bobdiwala S., Kyriacou C., Farren J., Mitchell-Jones N., Ayim F., et al. External validation of models to predict the outcome of pregnancies of unknown location: a multicentre cohort study. BJOG. 2021;128(3):552–562. doi: 10.1111/1471-0528.16497. [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Van Calster B., Abdallah Y., Guha S., Kirk E., Van Hoorde K., Condous G., et al. Rationalizing the management of pregnancies of unknown location: temporal and external validation of a risk prediction model on 1962 pregnancies. Hum. Reprod. 2013;28(3):609–616. doi: 10.1093/humrep/des440. [DOI] [PubMed] [Google Scholar]
11.Bobdiwala S., Guha S., Van Calster B., Ayim F., Mitchell-Jones N., Al-Memar M., et al. The clinical performance of the M4 decision support model to triage women with a pregnancy of unknown location as at low or high risk of complications. Hum. Reprod. 2016;31(7):1425–1435. doi: 10.1093/humrep/dew105. [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Bobdiwala S., Christodoulou E., Farren J., Mitchell-Jones N., Kyriacou C., Al-Memar M., et al. Triaging women with pregnancy of unknown location using two-step protocol including M6 model: clinical implementation study. Ultrasound Obstet. Gynecol. 2020;55(1):105–114. doi: 10.1002/uog.20420. [DOI] [PubMed] [Google Scholar]
13.Bobdiwala S., Kyriacou C., Christodoulou E., Farren J., Mitchell-Jones N., Al-Memar M., et al. Evaluating cut-off levels for progesterone, β human chorionic gonadotropin and β human chorionic gonadotropin ratio to exclude pregnancy viability in women with a pregnancy of unknown location: a prospective multicenter cohort study. Acta Obstet. Gynecol. Scand. 2022;101(1):46–55. doi: 10.1111/aogs.14295. [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Guha S., Ayim F., Ludlow J., Sayasneh A., Condous G., Kirk E., et al. Triaging pregnancies of unknown location: the performance of protocols based on single serum progesterone or repeated serum hCG levels. Hum. Reprod. 2014;29(5):938–945. doi: 10.1093/humrep/deu045. [DOI] [PubMed] [Google Scholar]
15.Van Calster B., Bobdiwala S., Guha S., Van Hoorde K., Al-Memar M., Harvey R., et al. Managing pregnancy of unknown location based on initial serum progesterone and serial serum hCG levels: development and validation of a two-step triage protocol. Ultrasound Obstet. Gynecol. 2016;48(5):642–649. doi: 10.1002/uog.15864. [DOI] [PubMed] [Google Scholar]
16.Senapati S., Barnhart K.T. Biomarkers for ectopic pregnancy and pregnancy of unknown location. Fertil. Steril. 2013;99(4):1107–1116. doi: 10.1016/j.fertnstert.2012.11.038. [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Tong S., Skubisz M.M., Horne A.W. Molecular diagnostics and therapeutics for ectopic pregnancy. Mol. Hum. Reprod. 2015;21(2):126–135. doi: 10.1093/molehr/gau084. [DOI] [PubMed] [Google Scholar]
18.Barnhart K., Speicher D.W. Molecular diagnosis of ectopic pregnancy. Expert Rev. Mol. Diagn. 2011;11(8):759–762. doi: 10.1586/erm.11.72. [DOI] [PubMed] [Google Scholar]
19.Kim S.H., MacIntyre D.A., Binkhamis R., Cook J., Sykes L., Bennett P.R., et al. Maternal plasma miRNAs as potential biomarkers for detecting risk of small-for-gestational-age births. EBioMedicine. 2020;62 doi: 10.1016/j.ebiom.2020.103145. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Ghafouri-Fard S., Shoorei H., Taheri M. The role of microRNAs in ectopic pregnancy: a concise review. Noncoding RNA Res. 2020;5(2):67–70. doi: 10.1016/j.ncrna.2020.04.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Cook J., Bennett P.R., Kim S.H., Teoh T.G., Sykes L., Kindinger L.M., et al. First trimester circulating microRNA biomarkers predictive of subsequent preterm delivery and cervical shortening. Sci. Rep. 2019;9(1):5861. doi: 10.1038/s41598-019-42166-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Cretoiu D., Xu J., Xiao J., Suciu N., Cretoiu S.M. Circulating microRNAs as potential molecular biomarkers in pathophysiological evolution of pregnancy. Dis. Markers. 2016;2016 doi: 10.1155/2016/3851054. [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Borden A., Kurian J., Nickoloff E., Yang Y., Troupes C.D., Ibetti J., et al. Transient introduction of miR-294 in the heart promotes cardiomyocyte cell cycle reentry after injury. Circ. Res. 2019;125(1):14–25. doi: 10.1161/CIRCRESAHA.118.314223. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Wang C., Ding S., Sun B., Shen L., Xiao L., Han Z., et al. Hsa-miR-4271 downregulates the expression of constitutive androstane receptor and enhances in vivo the sensitivity of non-small cell lung cancer to gefitinib. Pharmacol. Res. 2020;161 doi: 10.1016/j.phrs.2020.105110. [DOI] [PubMed] [Google Scholar]
25.Ahlbrecht J., Martino F., Pul R., Skripuletz T., Sühs K.W., Schauerte C., et al. Deregulation of microRNA-181c in cerebrospinal fluid of patients with clinically isolated syndrome is associated with early conversion to relapsing-remitting multiple sclerosis. Mult. Scler. 2016;22(9):1202–1214. doi: 10.1177/1352458515613641. [DOI] [PubMed] [Google Scholar]
26.Li X., Xiao J., Li K., Zhou Y. MiR-199-3p modulates the onset of puberty in rodents probably by regulating the expression of Kiss1 via the p38 MAPK pathway. Mol. Cell. Endocrinol. 2020;518 doi: 10.1016/j.mce.2020.110994. [DOI] [PubMed] [Google Scholar]
27.Liu L., Yuan L., Huang D., Han Q., Cai J., Wang S., et al. miR-126 regulates the progression of epithelial ovarian cancer in vitro and in vivo by targeting VEGF-A. Int. J. Oncol. 2020;57(3):825–834. doi: 10.3892/ijo.2020.5082. [DOI] [PubMed] [Google Scholar]
28.Saadat N., Puttabyatappa M., Elangovan V.R., Dou J., Ciarelli J.N., Thompson R.C., et al. Developmental programming: prenatal testosterone excess on liver and muscle coding and noncoding RNA in female sheep. Endocrinology. 2022;163(1) doi: 10.1210/endocr/bqab225. [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Tian L., Li Y., Wang C., Li Q. Let-7g-5p regulates mouse mammary cells differentiation and function by targeting PRKCA. J. Cell. Physiol. 2019;234(7):10101–10110. doi: 10.1002/jcp.27676. [DOI] [PubMed] [Google Scholar]
30.Zhao C., Du F., Zhao Y., Wang S., Qi L. Acute myeloid leukemia cells secrete microRNA-4532-containing exosomes to mediate normal hematopoiesis in hematopoietic stem cells by activating the LDOC1-dependent STAT3 signaling pathway. Stem Cell Res. Ther. 2019;10(1):384. doi: 10.1186/s13287-019-1475-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Xu T., Li L., Huang C., Li X., Peng Y., Li J. MicroRNA-323-3p with clinical potential in rheumatoid arthritis, Alzheimer's disease and ectopic pregnancy. Expert Opin. Ther. Targets. 2014;18(2):153–158. doi: 10.1517/14728222.2014.855201. [DOI] [PubMed] [Google Scholar]
32.Tamaru S., Kajihara T., Mizuno Y., Tochigi H., Ishihara O. Endometrial microRNAs and their aberrant expression patterns. Med. Mol. Morphol. 2020;53(3):131–140. doi: 10.1007/s00795-020-00252-8. [DOI] [PubMed] [Google Scholar]
33.Cerny K.L., Ribeiro R.A.C., Li Q., Matthews J.C., Bridges P.J. Effect of lipopolysaccharide on the expression of inflammatory mRNAs and microRNAs in the mouse oviduct. Reprod. Fertil. Dev. 2018;30(4):600–608. doi: 10.1071/RD17241. [DOI] [PubMed] [Google Scholar]
34.Gonzalez G., Behringer R.R. Dicer is required for female reproductive tract development and fertility in the mouse. Mol. Reprod. Dev. 2009;76(7):678–688. doi: 10.1002/mrd.21010. [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Marí-Alexandre J., Barceló-Molina M., Belmonte-López E., García-Oms J., Estellés A., Braza-Boïls A., et al. Micro-RNA profile and proteins in peritoneal fluid from women with endometriosis: their relationship with sterility. Fertil. Steril. 2018;109(4):675. doi: 10.1016/j.fertnstert.2017.11.036. 684.e2. [DOI] [PubMed] [Google Scholar]
36.Tang W., Chen O., Yao F., Cui L. miR-455 targets FABP4 to protect human endometrial stromal cells from cytotoxicity induced by hydrogen peroxide. Mol. Med. Rep. 2019;20(6):4781–4790. doi: 10.3892/mmr.2019.10727. [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Dvorská D., Višňovský J., Braný D., Danko J. [Leiomyomas of the uterine body: molecular-genetical aspects of formation and development] Ceská Gynekol. 2016;81(1):48–52. [PubMed] [Google Scholar]
38.Chao A., Tsai C.L., Wei P.C., Hsueh S., Chao A.S., Wang C.J., et al. Decreased expression of microRNA-199b increases protein levels of SET (protein phosphatase 2A inhibitor) in human choriocarcinoma. Cancer Lett. 2010;291(1):99–107. doi: 10.1016/j.canlet.2009.10.005. [DOI] [PubMed] [Google Scholar]
39.Miao J., Zhu Y., Xu L., Huang X., Zhou X. miR-181b-5p inhibits trophoblast cell migration and invasion through targeting S1PR1 in multiple abnormal trophoblast invasion-related events. Mol. Med. Rep. 2020;22(5):4442–4451. doi: 10.3892/mmr.2020.11515. [DOI] [PubMed] [Google Scholar]
40.Pang R.T., Leung C.O., Lee C.L., Lam K.K., Ye T.M., Chiu P.C., et al. MicroRNA-34a is a tumor suppressor in choriocarcinoma via regulation of Delta-like1. BMC Cancer. 2013;13:25. doi: 10.1186/1471-2407-13-25. [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Belot M.P., Nadéri K., Mille C., Boëlle P.Y., Benachi A., Bougnères P., et al. Role of DNA methylation at the placental RTL1 gene locus in type 1 diabetes. Pediatr. Diabetes. 2017;18(3):178–187. doi: 10.1111/pedi.12387. [DOI] [PubMed] [Google Scholar]
42.Cook J.R., MacIntyre D.A., Samara E., Kim S.H., Singh N., Johnson M.R., et al. Exogenous oxytocin modulates human myometrial microRNAs. Am. J. Obstet. Gynecol. 2015;213(1) doi: 10.1016/j.ajog.2015.03.015. 65.e1-65.e9. [DOI] [PubMed] [Google Scholar]
43.Dai W., Liu X. Circular RNA 0004904 promotes autophagy and regulates the fused in sarcoma/vascular endothelial growth factor axis in preeclampsia. Int. J. Mol. Med. 2021;47(6) doi: 10.3892/ijmm.2021.4944. [DOI] [PubMed] [Google Scholar]
44.Dong D., Fu N., Yang P. MiR-17 Downregulation by high glucose stabilizes thioredoxin-interacting protein and removes thioredoxin inhibition on ASK1 leading to apoptosis. Toxicol. Sci. 2016;150(1):84–96. doi: 10.1093/toxsci/kfv313. [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Kapur R.P., Berry J.E., Tsuchiya K.D., Opheim K.E. Activation of the chromosome 19q microRNA cluster in sporadic and androgenetic-biparental mosaicism-associated hepatic mesenchymal hamartoma. Pediatr. Dev. Pathol. 2014;17(2):75–84. doi: 10.2350/13-12-1415-OA.1. [DOI] [PubMed] [Google Scholar]
46.Saha S., Chakraborty S., Bhattacharya A., Biswas A., Ain R. MicroRNA regulation of transthyretin in trophoblast differentiation and intra-uterine growth restriction. Sci. Rep. 2017;7(1) doi: 10.1038/s41598-017-16566-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Sun M., Chen H., Liu J., Tong C., Meng T. MicroRNA-34a inhibits human trophoblast cell invasion by targeting MYC. BMC Cell Biol. 2015;16:21. doi: 10.1186/s12860-015-0068-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Teng L., Liu P., Song X., Wang H., Sun J., Yin Z. Long non-coding RNA nuclear-enriched abundant transcript 1 (NEAT1) represses proliferation of trophoblast cells in rats with preeclampsia via the microRNA-373/FLT1 axis. Med. Sci. Monit. 2020;26 doi: 10.12659/MSM.927305. [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Wu L., Song W.Y., Xie Y., Hu L.L., Hou X.M., Wang R., et al. miR-181a-5p suppresses invasion and migration of HTR-8/SVneo cells by directly targeting IGF2BP2. Cell Death Dis. 2018;9(2):16. doi: 10.1038/s41419-017-0045-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Zhang Y., Fei M., Xue G., Zhou Q., Jia Y., Li L., et al. Elevated levels of hypoxia-inducible microRNA-210 in pre-eclampsia: new insights into molecular mechanisms for the disease. J. Cell Mol. Med. 2012;16(2):249–259. doi: 10.1111/j.1582-4934.2011.01291.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Denomme M.M., McCallie B.R., Parks J.C., Schoolcraft W.B., Katz-Jaffe M.G. Alterations in the sperm histone-retained epigenome are associated with unexplained male factor infertility and poor blastocyst development in donor oocyte IVF cycles. Hum. Reprod. 2017;32(12):2443–2455. doi: 10.1093/humrep/dex317. [DOI] [PubMed] [Google Scholar]
52.Dominguez F., Moreno-Moya J.M., Lozoya T., Romero A., Martínez S., Monterde M., et al. Embryonic miRNA profiles of normal and ectopic pregnancies. PLoS One. 2014;9(7) doi: 10.1371/journal.pone.0102185. [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Galliano D., Pellicer A. MicroRNA and implantation. Fertil. Steril. 2014;101(6):1531–1544. doi: 10.1016/j.fertnstert.2014.04.023. [DOI] [PubMed] [Google Scholar]
54.Feng Y., Zou S., Weijdegård B., Chen J., Cong Q., Fernandez-Rodriguez J., et al. The onset of human ectopic pregnancy demonstrates a differential expression of miRNAs and their cognate targets in the fallopian tube. Int. J. Clin. Exp. Pathol. 2014;7(1):64–79. [PMC free article] [PubMed] [Google Scholar]
55.Kontomanolis E.N., Kalagasidou S., Fasoulakis Z. MicroRNAs as potential serum biomarkers for early detection of ectopic pregnancy. Cureus. 2018;10(3) doi: 10.7759/cureus.2344. [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Lozoya T., Domínguez F., Romero-Ruiz A., Steffani L., Martínez S., Monterde M., et al. The Lin28/Let-7 system in early human embryonic tissue and ectopic pregnancy. PLoS One. 2014;9(1) doi: 10.1371/journal.pone.0087698. [DOI] [PMC free article] [PubMed] [Google Scholar]
57.Lu Q., Yan Q., Xu F., Li Y., Zhao W., Wu C., et al. MicroRNA-873 is a potential serum biomarker for the detection of ectopic pregnancy. Cell. Physiol. Biochem. 2017;41(6):2513–2522. doi: 10.1159/000475946. [DOI] [PubMed] [Google Scholar]
58.Miura K., Higashijima A., Mishima H., Miura S., Kitajima M., Kaneuchi M., et al. Pregnancy-associated microRNAs in plasma as potential molecular markers of ectopic pregnancy. Fertil. Steril. 2015;103(5) doi: 10.1016/j.fertnstert.2015.01.041. 1202-1208.e1. [DOI] [PubMed] [Google Scholar]
59.Romero-Ruiz A., Avendaño M.S., Dominguez F., Lozoya T., Molina-Abril H., Sangiao-Alvarellos S., et al. Deregulation of miR-324/KISS1/kisspeptin in early ectopic pregnancy: mechanistic findings with clinical and diagnostic implications. Am. J. Obstet. Gynecol. 2019;220(5) doi: 10.1016/j.ajog.2019.01.228. 480.e1-480.e17. [DOI] [PubMed] [Google Scholar]
60.Sun J., Deng G., Ruan X., Chen S., Liao H., Liu X., et al. Exosomal microRNAs in serum as potential biomarkers for ectopic pregnancy. BioMed Res. Int. 2020;2020 doi: 10.1155/2020/3521859. [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Zhao Z., Zhao Q., Warrick J., Lockwood C.M., Woodworth A., Moley K.H., et al. Circulating microRNA miR-323-3p as a biomarker of ectopic pregnancy. Clin. Chem. 2012;58(5):896–905. doi: 10.1373/clinchem.2011.179283. [DOI] [PMC free article] [PubMed] [Google Scholar]
62.Dennis L., Rhodes M., Maclean K. 2015. Targeted miRNA discovery and validation using the nCounter® platform.https://nanostring.com/wp-content/uploads/LBL-10112-01_Human_miRNA.pdf Available from. [Google Scholar]
63.Andersen C.L., Jensen J.L., Ørntoft T.F. Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res. 2004;64(15):5245–5250. doi: 10.1158/0008-5472.CAN-04-0496. [DOI] [PubMed] [Google Scholar]
64.Untergasser A., Ruijter J.M., Benes V., van den Hoff M.J.B. Web-based LinRegPCR: application for the visualization and analysis of (RT)-qPCR amplification and melting data. BMC Bioinf. 2021;22(1):398. doi: 10.1186/s12859-021-04306-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
65.Tokar T., Pastrello C., Rossos A.E.M., Abovsky M., Hauschild A.C., Tsay M., et al. mirDIP 4.1-integrative database of human microRNA target predictions. Nucleic Acids Res. 2018;46(D1):D360–D370. doi: 10.1093/nar/gkx1144. [DOI] [PMC free article] [PubMed] [Google Scholar]
66.Shirdel E.A., Xie W., Mak T.W., Jurisica I. NAViGaTing the micronome--using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs. PLoS One. 2011;6(2) doi: 10.1371/journal.pone.0017429. [DOI] [PMC free article] [PubMed] [Google Scholar]
67.Whigham C.A., MacDonald T.M., Walker S.P., Hannan N.J., Tong S., Kaitu'u-Lino T.J. The untapped potential of placenta-enriched molecules for diagnostic and therapeutic development. Placenta. 2019;84:28–31. doi: 10.1016/j.placenta.2019.02.002. [DOI] [PubMed] [Google Scholar]
68.Jassal B., Matthews L., Viteri G., Gong C., Lorente P., Fabregat A., et al. The reactome pathway knowledgebase. Nucleic Acids Res. 2020;48(D1):D498–D503. doi: 10.1093/nar/gkz1031. [DOI] [PMC free article] [PubMed] [Google Scholar]
69.Sidiropoulos K., Viteri G., Sevilla C., Jupe S., Webber M., Orlic-Milacic M., et al. Reactome enhanced pathway visualization. Bioinformatics. 2017;33(21):3461–3467. doi: 10.1093/bioinformatics/btx441. [DOI] [PMC free article] [PubMed] [Google Scholar]
70.Fabregat A., Sidiropoulos K., Viteri G., Forner O., Marin-Garcia P., Arnau V., et al. Reactome pathway analysis: a high-performance in-memory approach. BMC Bioinf. 2017;18(1):142. doi: 10.1186/s12859-017-1559-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
71.Fabregat A., Sidiropoulos K., Viteri G., Marin-Garcia P., Ping P., Stein L., et al. Reactome diagram viewer: data structures and strategies to boost performance. Bioinformatics. 2018;34(7):1208–1214. doi: 10.1093/bioinformatics/btx752. [DOI] [PMC free article] [PubMed] [Google Scholar]
72.Fabregat A., Korninger F., Viteri G., Sidiropoulos K., Marin-Garcia P., Ping P., et al. Reactome graph database: efficient access to complex pathway data. PLoS Comput. Biol. 2018;14(1) doi: 10.1371/journal.pcbi.1005968. [DOI] [PMC free article] [PubMed] [Google Scholar]
73.Wu G., Haw R. Functional interaction network construction and analysis for disease discovery. Methods Mol. Biol. 2017;1558:235–253. doi: 10.1007/978-1-4939-6783-4_11. [DOI] [PubMed] [Google Scholar]
74.Kyriacou C., Kim S., Bobdiwala S., Bennett P., Bourne T., Terzidou V. The assessment of microRNA expression in pregnancies classified as a pregnancy of unknown location using transvaginal ultrasonography. Ultrasound Obstet. Gynecol. 2019;54(Suppl. 1):1. [Google Scholar]
75.Kufuor R., Kyriacou C., Kim S., Bobdiwala S., Fourie H., Bennett P., et al. The assessment of circulating biomarker expression in pregnancy of unknown location and ectopic pregnancy: a pilot study. Ultrasound Obstet. Gynecol. 2020;56(Suppl. 1):134–135. [Google Scholar]
76.Sullivan-Pyke C., Sansone S., Jou J., Koelper N., Stentz N., Takacs P. Small non-coding RNA as a serum biomarker for non-viable and ectopic pregnancy. Fertil. Steril. 2018;110 [Google Scholar]
77.Zhang S., Sun Q., Jiang X., Gao F. Clinical significance of expression of hsa-mir-1247 and hsa-mir-1269a in ectopic pregnancy due to salpingitis. Exp. Ther. Med. 2018;15(6):4901–4905. doi: 10.3892/etm.2018.5998. [DOI] [PMC free article] [PubMed] [Google Scholar]
78.Balaguer N., Moreno I., Herrero M., Gonzáléz-Monfort M., Vilella F., Simón C. MicroRNA-30d deficiency during preconception affects endometrial receptivity by decreasing implantation rates and impairing fetal growth. Am. J. Obstet. Gynecol. 2019;221(1):46.e1–46.e16. doi: 10.1016/j.ajog.2019.02.047. [DOI] [PubMed] [Google Scholar]
79.Kottawatta K.S., So K.H., Kodithuwakku S.P., Ng E.H., Yeung W.S., Lee K.F. MicroRNA-212 regulates the expression of olfactomedin 1 and C-terminal binding protein 1 in human endometrial epithelial cells to enhance spheroid attachment in vitro. Biol. Reprod. 2015;93(5):109. doi: 10.1095/biolreprod.115.131334. [DOI] [PubMed] [Google Scholar]
80.Abu-Halima M., Khaizaran Z.A., Ayesh B.M., Fischer U., Khaizaran S.A., Al-Battah F., et al. MicroRNAs in combined spent culture media and sperm are associated with embryo quality and pregnancy outcome. Fertil. Steril. 2020;113(5) doi: 10.1016/j.fertnstert.2019.12.028. 970–980.e2. [DOI] [PubMed] [Google Scholar]
81.Fang F., Li Z., Yu J., Long Y., Zhao Q., Ding X., et al. MicroRNAs secreted by human embryos could be potential biomarkers for clinical outcomes of assisted reproductive technology. J. Adv. Res. 2021;31:25–34. doi: 10.1016/j.jare.2021.01.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
82.Feng F., Lei L., Liao J., Huang X., Shao Y. Circ_0060731 mediated miR-21-5p-PDCD4/ESR1 pathway to induce apoptosis of placental trophoblasts in intrahepatic cholestasis of pregnancy. Tissue Cell. 2022;76 doi: 10.1016/j.tice.2022.101771. [DOI] [PubMed] [Google Scholar]
83.Hromadnikova I., Kotlabova K., Dvorakova L., Krofta L., Sirc J. Postnatal expression profile of microRNAs associated with cardiovascular and cerebrovascular diseases in children at the age of 3 to 11 years in relation to previous occurrence of pregnancy-related complications. Int. J. Mol. Sci. 2019;20(3) doi: 10.3390/ijms20030654. [DOI] [PMC free article] [PubMed] [Google Scholar]
84.Hromadnikova I., Kotlabova K., Dvorakova L., Krofta L. Diabetes mellitus and cardiovascular risk assessment in mothers with a history of gestational diabetes mellitus based on postpartal expression profile of microRNAs associated with diabetes mellitus and cardiovascular and cerebrovascular diseases. Int. J. Mol. Sci. 2020;21(7) doi: 10.3390/ijms21072437. [DOI] [PMC free article] [PubMed] [Google Scholar]
85.Hromadnikova I., Kotlabova K., Krofta L. A history of preterm delivery Is associated with aberrant postpartal microRNA expression profiles in mothers with an absence of other pregnancy-related complications. Int. J. Mol. Sci. 2021;22(8) doi: 10.3390/ijms22084033. [DOI] [PMC free article] [PubMed] [Google Scholar]
86.Hromadnikova I., Kotlabova K., Krofta L. First trimester prediction of preterm delivery in the absence of other pregnancy-related complications using cardiovascular-disease associated microRNA biomarkers. Int. J. Mol. Sci. 2022;23(7) doi: 10.3390/ijms23073951. [DOI] [PMC free article] [PubMed] [Google Scholar]
87.Hua R., Zhang X., Li W., Lian W., Liu Q., Gao D., et al. Ssc-miR-21-5p regulates endometrial epithelial cell proliferation, apoptosis and migration via the PDCD4/AKT pathway. J. Cell Sci. 2020;133(23) doi: 10.1242/jcs.248898. [DOI] [PubMed] [Google Scholar]
88.Karakaya C., Guzeloglu-Kayisli O., Uyar A., Kallen A.N., Babayev E., Bozkurt N., et al. Poor ovarian response in women undergoing in vitro fertilization is associated with altered microRNA expression in cumulus cells. Fertil. Steril. 2015;103(6):1469–1476. doi: 10.1016/j.fertnstert.2015.02.035. e1-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
89.Khan H.L., Bhatti S., Abbas S., Kaloglu C., Isa A.M., Younas H., et al. Extracellular microRNAs: key players to explore the outcomes of in vitro fertilization. Reprod. Biol. Endocrinol. 2021;19(1):72. doi: 10.1186/s12958-021-00754-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
90.Kim K.H., Kim E.Y., Kim G.J., Ko J.J., Cha K.Y., Koong M.K., et al. Human placenta-derived mesenchymal stem cells stimulate ovarian function via miR-145 and bone morphogenetic protein signaling in aged rats. Stem Cell Res. Ther. 2020;11(1):472. doi: 10.1186/s13287-020-01988-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
91.Kochhar P., Dwarkanath P., Ravikumar G., Thomas A., Crasta J., Thomas T., et al. Placental expression of miR-21-5p, miR-210-3p and miR-141-3p: relation to human fetoplacental growth. Eur. J. Clin. Nutr. 2022;76(5):730–738. doi: 10.1038/s41430-021-01017-x. [DOI] [PubMed] [Google Scholar]
92.Kolkova Z., Holubekova V., Grendar M., Nachajova M., Zubor P., Pribulova T., et al. Association of circulating miRNA expression with preeclampsia, its onset, and severity. Diagnostics. 2021;11(3) doi: 10.3390/diagnostics11030476. [DOI] [PMC free article] [PubMed] [Google Scholar]
93.Peñaloza E., Soto-Carrasco G., Krause B.J. MiR-21-5p directly contributes to regulating eNOS expression in human artery endothelial cells under normoxia and hypoxia. Biochem. Pharmacol. 2020;182 doi: 10.1016/j.bcp.2020.114288. [DOI] [PubMed] [Google Scholar]
94.Sadri M., Shu J., Kachman S.D., Cui J., Zempleni J. Milk exosomes and miRNA cross the placenta and promote embryo survival in mice. Reproduction. 2020;160(4):501–509. doi: 10.1530/REP-19-0521. [DOI] [PubMed] [Google Scholar]
95.Fan X., Lou J., Zheng X., Wang Y., Wang J., Luo M., et al. Interference with lncRNA NEAT1 promotes the proliferation, migration, and invasion of trophoblasts by upregulating miR-411-5p and inhibiting PTEN expression. Immunopharmacol. Immunotoxicol. 2021;43(3):334–342. doi: 10.1080/08923973.2021.1910834. [DOI] [PubMed] [Google Scholar]
96.Pritchard N., Kaitu'u-Lino T., Harris L., Tong S., Hannan N. Nanoparticles in pregnancy: the next frontier in reproductive therapeutics. Hum. Reprod. Update. 2021;27(2):280–304. doi: 10.1093/humupd/dmaa049. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Multimedia component 1

mmc1.docx^{(161.1KB, docx)}

Multimedia component 2

mmc2.xlsx^{(329.4KB, xlsx)}

Multimedia component 3

mmc3.doc^{(90.5KB, doc)}

Data Availability Statement

[bib1] 1.Bobdiwala S., Al-Memar M., Farren J., Bourne T. Factors to consider in pregnancy of unknown location. Womens Health (Lond). 2017;13(2):27–33. doi: 10.1177/1745505717709677. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib2] 2.Banerjee S., Aslam N., Woelfer B., Lawrence A., Elson J., Jurkovic D. Expectant management of early pregnancies of unknown location: a prospective evaluation of methods to predict spontaneous resolution of pregnancy. BJOG. 2001;108(2):158–163. doi: 10.1111/j.1471-0528.2001.00031.x. [DOI] [PubMed] [Google Scholar]

[bib3] 3.Mol B.W., Hajenius P.J., Engelsbel S., Ankum W.M., Van der Veen F., Hemrika D.J., et al. Serum human chorionic gonadotropin measurement in the diagnosis of ectopic pregnancy when transvaginal sonography is inconclusive. Fertil. Steril. 1998;70(5):972–981. doi: 10.1016/s0015-0282(98)00278-7. [DOI] [PubMed] [Google Scholar]

[bib4] 4.Kirk E., Condous G., Van Calster B., Van Huffel S., Timmerman D., Bourne T. Rationalizing the follow-up of pregnancies of unknown location. Hum. Reprod. 2007;22(6):1744–1750. doi: 10.1093/humrep/dem073. [DOI] [PubMed] [Google Scholar]

[bib5] 5.Cordina M., Schramm-Gajraj K., Ross J.A., Lautman K., Jurkovic D. Introduction of a single visit protocol in the management of selected patients with pregnancy of unknown location: a prospective study. BJOG. 2011;118(6):693–697. doi: 10.1111/j.1471-0528.2011.02893.x. [DOI] [PubMed] [Google Scholar]

[bib6] 6.Kirk E., Bottomley C., Bourne T. Diagnosing ectopic pregnancy and current concepts in the management of pregnancy of unknown location. Hum. Reprod. Update. 2014;20(2):250–261. doi: 10.1093/humupd/dmt047. [DOI] [PubMed] [Google Scholar]

[bib7] 7.Webster K., Eadon H., Fishburn S., Kumar G., Committee G. Ectopic pregnancy and miscarriage: diagnosis and initial management: summary of updated NICE guidance. Br. Med. J. 2019;367 doi: 10.1136/bmj.l6283. [DOI] [PubMed] [Google Scholar]

[bib8] 8.Bobdiwala S., Saso S., Verbakel J.Y., Al-Memar M., Van Calster B., Timmerman D., et al. Diagnostic protocols for the management of pregnancy of unknown location: a systematic review and meta-analysis. BJOG. 2019;126(2):190–198. doi: 10.1111/1471-0528.15442. [DOI] [PubMed] [Google Scholar]

[bib9] 9.Christodoulou E., Bobdiwala S., Kyriacou C., Farren J., Mitchell-Jones N., Ayim F., et al. External validation of models to predict the outcome of pregnancies of unknown location: a multicentre cohort study. BJOG. 2021;128(3):552–562. doi: 10.1111/1471-0528.16497. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib10] 10.Van Calster B., Abdallah Y., Guha S., Kirk E., Van Hoorde K., Condous G., et al. Rationalizing the management of pregnancies of unknown location: temporal and external validation of a risk prediction model on 1962 pregnancies. Hum. Reprod. 2013;28(3):609–616. doi: 10.1093/humrep/des440. [DOI] [PubMed] [Google Scholar]

[bib11] 11.Bobdiwala S., Guha S., Van Calster B., Ayim F., Mitchell-Jones N., Al-Memar M., et al. The clinical performance of the M4 decision support model to triage women with a pregnancy of unknown location as at low or high risk of complications. Hum. Reprod. 2016;31(7):1425–1435. doi: 10.1093/humrep/dew105. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib12] 12.Bobdiwala S., Christodoulou E., Farren J., Mitchell-Jones N., Kyriacou C., Al-Memar M., et al. Triaging women with pregnancy of unknown location using two-step protocol including M6 model: clinical implementation study. Ultrasound Obstet. Gynecol. 2020;55(1):105–114. doi: 10.1002/uog.20420. [DOI] [PubMed] [Google Scholar]

[bib13] 13.Bobdiwala S., Kyriacou C., Christodoulou E., Farren J., Mitchell-Jones N., Al-Memar M., et al. Evaluating cut-off levels for progesterone, β human chorionic gonadotropin and β human chorionic gonadotropin ratio to exclude pregnancy viability in women with a pregnancy of unknown location: a prospective multicenter cohort study. Acta Obstet. Gynecol. Scand. 2022;101(1):46–55. doi: 10.1111/aogs.14295. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib14] 14.Guha S., Ayim F., Ludlow J., Sayasneh A., Condous G., Kirk E., et al. Triaging pregnancies of unknown location: the performance of protocols based on single serum progesterone or repeated serum hCG levels. Hum. Reprod. 2014;29(5):938–945. doi: 10.1093/humrep/deu045. [DOI] [PubMed] [Google Scholar]

[bib15] 15.Van Calster B., Bobdiwala S., Guha S., Van Hoorde K., Al-Memar M., Harvey R., et al. Managing pregnancy of unknown location based on initial serum progesterone and serial serum hCG levels: development and validation of a two-step triage protocol. Ultrasound Obstet. Gynecol. 2016;48(5):642–649. doi: 10.1002/uog.15864. [DOI] [PubMed] [Google Scholar]

[bib16] 16.Senapati S., Barnhart K.T. Biomarkers for ectopic pregnancy and pregnancy of unknown location. Fertil. Steril. 2013;99(4):1107–1116. doi: 10.1016/j.fertnstert.2012.11.038. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib17] 17.Tong S., Skubisz M.M., Horne A.W. Molecular diagnostics and therapeutics for ectopic pregnancy. Mol. Hum. Reprod. 2015;21(2):126–135. doi: 10.1093/molehr/gau084. [DOI] [PubMed] [Google Scholar]

[bib18] 18.Barnhart K., Speicher D.W. Molecular diagnosis of ectopic pregnancy. Expert Rev. Mol. Diagn. 2011;11(8):759–762. doi: 10.1586/erm.11.72. [DOI] [PubMed] [Google Scholar]

[bib19] 19.Kim S.H., MacIntyre D.A., Binkhamis R., Cook J., Sykes L., Bennett P.R., et al. Maternal plasma miRNAs as potential biomarkers for detecting risk of small-for-gestational-age births. EBioMedicine. 2020;62 doi: 10.1016/j.ebiom.2020.103145. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib20] 20.Ghafouri-Fard S., Shoorei H., Taheri M. The role of microRNAs in ectopic pregnancy: a concise review. Noncoding RNA Res. 2020;5(2):67–70. doi: 10.1016/j.ncrna.2020.04.002. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib21] 21.Cook J., Bennett P.R., Kim S.H., Teoh T.G., Sykes L., Kindinger L.M., et al. First trimester circulating microRNA biomarkers predictive of subsequent preterm delivery and cervical shortening. Sci. Rep. 2019;9(1):5861. doi: 10.1038/s41598-019-42166-1. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib22] 22.Cretoiu D., Xu J., Xiao J., Suciu N., Cretoiu S.M. Circulating microRNAs as potential molecular biomarkers in pathophysiological evolution of pregnancy. Dis. Markers. 2016;2016 doi: 10.1155/2016/3851054. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib23] 23.Borden A., Kurian J., Nickoloff E., Yang Y., Troupes C.D., Ibetti J., et al. Transient introduction of miR-294 in the heart promotes cardiomyocyte cell cycle reentry after injury. Circ. Res. 2019;125(1):14–25. doi: 10.1161/CIRCRESAHA.118.314223. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib24] 24.Wang C., Ding S., Sun B., Shen L., Xiao L., Han Z., et al. Hsa-miR-4271 downregulates the expression of constitutive androstane receptor and enhances in vivo the sensitivity of non-small cell lung cancer to gefitinib. Pharmacol. Res. 2020;161 doi: 10.1016/j.phrs.2020.105110. [DOI] [PubMed] [Google Scholar]

[bib25] 25.Ahlbrecht J., Martino F., Pul R., Skripuletz T., Sühs K.W., Schauerte C., et al. Deregulation of microRNA-181c in cerebrospinal fluid of patients with clinically isolated syndrome is associated with early conversion to relapsing-remitting multiple sclerosis. Mult. Scler. 2016;22(9):1202–1214. doi: 10.1177/1352458515613641. [DOI] [PubMed] [Google Scholar]

[bib26] 26.Li X., Xiao J., Li K., Zhou Y. MiR-199-3p modulates the onset of puberty in rodents probably by regulating the expression of Kiss1 via the p38 MAPK pathway. Mol. Cell. Endocrinol. 2020;518 doi: 10.1016/j.mce.2020.110994. [DOI] [PubMed] [Google Scholar]

[bib27] 27.Liu L., Yuan L., Huang D., Han Q., Cai J., Wang S., et al. miR-126 regulates the progression of epithelial ovarian cancer in vitro and in vivo by targeting VEGF-A. Int. J. Oncol. 2020;57(3):825–834. doi: 10.3892/ijo.2020.5082. [DOI] [PubMed] [Google Scholar]

[bib28] 28.Saadat N., Puttabyatappa M., Elangovan V.R., Dou J., Ciarelli J.N., Thompson R.C., et al. Developmental programming: prenatal testosterone excess on liver and muscle coding and noncoding RNA in female sheep. Endocrinology. 2022;163(1) doi: 10.1210/endocr/bqab225. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib29] 29.Tian L., Li Y., Wang C., Li Q. Let-7g-5p regulates mouse mammary cells differentiation and function by targeting PRKCA. J. Cell. Physiol. 2019;234(7):10101–10110. doi: 10.1002/jcp.27676. [DOI] [PubMed] [Google Scholar]

[bib30] 30.Zhao C., Du F., Zhao Y., Wang S., Qi L. Acute myeloid leukemia cells secrete microRNA-4532-containing exosomes to mediate normal hematopoiesis in hematopoietic stem cells by activating the LDOC1-dependent STAT3 signaling pathway. Stem Cell Res. Ther. 2019;10(1):384. doi: 10.1186/s13287-019-1475-7. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib31] 31.Xu T., Li L., Huang C., Li X., Peng Y., Li J. MicroRNA-323-3p with clinical potential in rheumatoid arthritis, Alzheimer's disease and ectopic pregnancy. Expert Opin. Ther. Targets. 2014;18(2):153–158. doi: 10.1517/14728222.2014.855201. [DOI] [PubMed] [Google Scholar]

[bib32] 32.Tamaru S., Kajihara T., Mizuno Y., Tochigi H., Ishihara O. Endometrial microRNAs and their aberrant expression patterns. Med. Mol. Morphol. 2020;53(3):131–140. doi: 10.1007/s00795-020-00252-8. [DOI] [PubMed] [Google Scholar]

[bib33] 33.Cerny K.L., Ribeiro R.A.C., Li Q., Matthews J.C., Bridges P.J. Effect of lipopolysaccharide on the expression of inflammatory mRNAs and microRNAs in the mouse oviduct. Reprod. Fertil. Dev. 2018;30(4):600–608. doi: 10.1071/RD17241. [DOI] [PubMed] [Google Scholar]

[bib34] 34.Gonzalez G., Behringer R.R. Dicer is required for female reproductive tract development and fertility in the mouse. Mol. Reprod. Dev. 2009;76(7):678–688. doi: 10.1002/mrd.21010. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib35] 35.Marí-Alexandre J., Barceló-Molina M., Belmonte-López E., García-Oms J., Estellés A., Braza-Boïls A., et al. Micro-RNA profile and proteins in peritoneal fluid from women with endometriosis: their relationship with sterility. Fertil. Steril. 2018;109(4):675. doi: 10.1016/j.fertnstert.2017.11.036. 684.e2. [DOI] [PubMed] [Google Scholar]

[bib36] 36.Tang W., Chen O., Yao F., Cui L. miR-455 targets FABP4 to protect human endometrial stromal cells from cytotoxicity induced by hydrogen peroxide. Mol. Med. Rep. 2019;20(6):4781–4790. doi: 10.3892/mmr.2019.10727. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib37] 37.Dvorská D., Višňovský J., Braný D., Danko J. [Leiomyomas of the uterine body: molecular-genetical aspects of formation and development] Ceská Gynekol. 2016;81(1):48–52. [PubMed] [Google Scholar]

[bib38] 38.Chao A., Tsai C.L., Wei P.C., Hsueh S., Chao A.S., Wang C.J., et al. Decreased expression of microRNA-199b increases protein levels of SET (protein phosphatase 2A inhibitor) in human choriocarcinoma. Cancer Lett. 2010;291(1):99–107. doi: 10.1016/j.canlet.2009.10.005. [DOI] [PubMed] [Google Scholar]

[bib39] 39.Miao J., Zhu Y., Xu L., Huang X., Zhou X. miR-181b-5p inhibits trophoblast cell migration and invasion through targeting S1PR1 in multiple abnormal trophoblast invasion-related events. Mol. Med. Rep. 2020;22(5):4442–4451. doi: 10.3892/mmr.2020.11515. [DOI] [PubMed] [Google Scholar]

[bib40] 40.Pang R.T., Leung C.O., Lee C.L., Lam K.K., Ye T.M., Chiu P.C., et al. MicroRNA-34a is a tumor suppressor in choriocarcinoma via regulation of Delta-like1. BMC Cancer. 2013;13:25. doi: 10.1186/1471-2407-13-25. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib41] 41.Belot M.P., Nadéri K., Mille C., Boëlle P.Y., Benachi A., Bougnères P., et al. Role of DNA methylation at the placental RTL1 gene locus in type 1 diabetes. Pediatr. Diabetes. 2017;18(3):178–187. doi: 10.1111/pedi.12387. [DOI] [PubMed] [Google Scholar]

[bib42] 42.Cook J.R., MacIntyre D.A., Samara E., Kim S.H., Singh N., Johnson M.R., et al. Exogenous oxytocin modulates human myometrial microRNAs. Am. J. Obstet. Gynecol. 2015;213(1) doi: 10.1016/j.ajog.2015.03.015. 65.e1-65.e9. [DOI] [PubMed] [Google Scholar]

[bib43] 43.Dai W., Liu X. Circular RNA 0004904 promotes autophagy and regulates the fused in sarcoma/vascular endothelial growth factor axis in preeclampsia. Int. J. Mol. Med. 2021;47(6) doi: 10.3892/ijmm.2021.4944. [DOI] [PubMed] [Google Scholar]

[bib44] 44.Dong D., Fu N., Yang P. MiR-17 Downregulation by high glucose stabilizes thioredoxin-interacting protein and removes thioredoxin inhibition on ASK1 leading to apoptosis. Toxicol. Sci. 2016;150(1):84–96. doi: 10.1093/toxsci/kfv313. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib45] 45.Kapur R.P., Berry J.E., Tsuchiya K.D., Opheim K.E. Activation of the chromosome 19q microRNA cluster in sporadic and androgenetic-biparental mosaicism-associated hepatic mesenchymal hamartoma. Pediatr. Dev. Pathol. 2014;17(2):75–84. doi: 10.2350/13-12-1415-OA.1. [DOI] [PubMed] [Google Scholar]

[bib46] 46.Saha S., Chakraborty S., Bhattacharya A., Biswas A., Ain R. MicroRNA regulation of transthyretin in trophoblast differentiation and intra-uterine growth restriction. Sci. Rep. 2017;7(1) doi: 10.1038/s41598-017-16566-0. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib47] 47.Sun M., Chen H., Liu J., Tong C., Meng T. MicroRNA-34a inhibits human trophoblast cell invasion by targeting MYC. BMC Cell Biol. 2015;16:21. doi: 10.1186/s12860-015-0068-2. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib48] 48.Teng L., Liu P., Song X., Wang H., Sun J., Yin Z. Long non-coding RNA nuclear-enriched abundant transcript 1 (NEAT1) represses proliferation of trophoblast cells in rats with preeclampsia via the microRNA-373/FLT1 axis. Med. Sci. Monit. 2020;26 doi: 10.12659/MSM.927305. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib49] 49.Wu L., Song W.Y., Xie Y., Hu L.L., Hou X.M., Wang R., et al. miR-181a-5p suppresses invasion and migration of HTR-8/SVneo cells by directly targeting IGF2BP2. Cell Death Dis. 2018;9(2):16. doi: 10.1038/s41419-017-0045-0. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib50] 50.Zhang Y., Fei M., Xue G., Zhou Q., Jia Y., Li L., et al. Elevated levels of hypoxia-inducible microRNA-210 in pre-eclampsia: new insights into molecular mechanisms for the disease. J. Cell Mol. Med. 2012;16(2):249–259. doi: 10.1111/j.1582-4934.2011.01291.x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib51] 51.Denomme M.M., McCallie B.R., Parks J.C., Schoolcraft W.B., Katz-Jaffe M.G. Alterations in the sperm histone-retained epigenome are associated with unexplained male factor infertility and poor blastocyst development in donor oocyte IVF cycles. Hum. Reprod. 2017;32(12):2443–2455. doi: 10.1093/humrep/dex317. [DOI] [PubMed] [Google Scholar]

[bib52] 52.Dominguez F., Moreno-Moya J.M., Lozoya T., Romero A., Martínez S., Monterde M., et al. Embryonic miRNA profiles of normal and ectopic pregnancies. PLoS One. 2014;9(7) doi: 10.1371/journal.pone.0102185. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib53] 53.Galliano D., Pellicer A. MicroRNA and implantation. Fertil. Steril. 2014;101(6):1531–1544. doi: 10.1016/j.fertnstert.2014.04.023. [DOI] [PubMed] [Google Scholar]

[bib54] 54.Feng Y., Zou S., Weijdegård B., Chen J., Cong Q., Fernandez-Rodriguez J., et al. The onset of human ectopic pregnancy demonstrates a differential expression of miRNAs and their cognate targets in the fallopian tube. Int. J. Clin. Exp. Pathol. 2014;7(1):64–79. [PMC free article] [PubMed] [Google Scholar]

[bib55] 55.Kontomanolis E.N., Kalagasidou S., Fasoulakis Z. MicroRNAs as potential serum biomarkers for early detection of ectopic pregnancy. Cureus. 2018;10(3) doi: 10.7759/cureus.2344. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib56] 56.Lozoya T., Domínguez F., Romero-Ruiz A., Steffani L., Martínez S., Monterde M., et al. The Lin28/Let-7 system in early human embryonic tissue and ectopic pregnancy. PLoS One. 2014;9(1) doi: 10.1371/journal.pone.0087698. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib57] 57.Lu Q., Yan Q., Xu F., Li Y., Zhao W., Wu C., et al. MicroRNA-873 is a potential serum biomarker for the detection of ectopic pregnancy. Cell. Physiol. Biochem. 2017;41(6):2513–2522. doi: 10.1159/000475946. [DOI] [PubMed] [Google Scholar]

[bib58] 58.Miura K., Higashijima A., Mishima H., Miura S., Kitajima M., Kaneuchi M., et al. Pregnancy-associated microRNAs in plasma as potential molecular markers of ectopic pregnancy. Fertil. Steril. 2015;103(5) doi: 10.1016/j.fertnstert.2015.01.041. 1202-1208.e1. [DOI] [PubMed] [Google Scholar]

[bib59] 59.Romero-Ruiz A., Avendaño M.S., Dominguez F., Lozoya T., Molina-Abril H., Sangiao-Alvarellos S., et al. Deregulation of miR-324/KISS1/kisspeptin in early ectopic pregnancy: mechanistic findings with clinical and diagnostic implications. Am. J. Obstet. Gynecol. 2019;220(5) doi: 10.1016/j.ajog.2019.01.228. 480.e1-480.e17. [DOI] [PubMed] [Google Scholar]

[bib60] 60.Sun J., Deng G., Ruan X., Chen S., Liao H., Liu X., et al. Exosomal microRNAs in serum as potential biomarkers for ectopic pregnancy. BioMed Res. Int. 2020;2020 doi: 10.1155/2020/3521859. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib61] 61.Zhao Z., Zhao Q., Warrick J., Lockwood C.M., Woodworth A., Moley K.H., et al. Circulating microRNA miR-323-3p as a biomarker of ectopic pregnancy. Clin. Chem. 2012;58(5):896–905. doi: 10.1373/clinchem.2011.179283. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib62] 62.Dennis L., Rhodes M., Maclean K. 2015. Targeted miRNA discovery and validation using the nCounter® platform.https://nanostring.com/wp-content/uploads/LBL-10112-01_Human_miRNA.pdf Available from. [Google Scholar]

[bib63] 63.Andersen C.L., Jensen J.L., Ørntoft T.F. Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res. 2004;64(15):5245–5250. doi: 10.1158/0008-5472.CAN-04-0496. [DOI] [PubMed] [Google Scholar]

[bib64] 64.Untergasser A., Ruijter J.M., Benes V., van den Hoff M.J.B. Web-based LinRegPCR: application for the visualization and analysis of (RT)-qPCR amplification and melting data. BMC Bioinf. 2021;22(1):398. doi: 10.1186/s12859-021-04306-1. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib65] 65.Tokar T., Pastrello C., Rossos A.E.M., Abovsky M., Hauschild A.C., Tsay M., et al. mirDIP 4.1-integrative database of human microRNA target predictions. Nucleic Acids Res. 2018;46(D1):D360–D370. doi: 10.1093/nar/gkx1144. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib66] 66.Shirdel E.A., Xie W., Mak T.W., Jurisica I. NAViGaTing the micronome--using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs. PLoS One. 2011;6(2) doi: 10.1371/journal.pone.0017429. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib67] 67.Whigham C.A., MacDonald T.M., Walker S.P., Hannan N.J., Tong S., Kaitu'u-Lino T.J. The untapped potential of placenta-enriched molecules for diagnostic and therapeutic development. Placenta. 2019;84:28–31. doi: 10.1016/j.placenta.2019.02.002. [DOI] [PubMed] [Google Scholar]

[bib68] 68.Jassal B., Matthews L., Viteri G., Gong C., Lorente P., Fabregat A., et al. The reactome pathway knowledgebase. Nucleic Acids Res. 2020;48(D1):D498–D503. doi: 10.1093/nar/gkz1031. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib69] 69.Sidiropoulos K., Viteri G., Sevilla C., Jupe S., Webber M., Orlic-Milacic M., et al. Reactome enhanced pathway visualization. Bioinformatics. 2017;33(21):3461–3467. doi: 10.1093/bioinformatics/btx441. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib70] 70.Fabregat A., Sidiropoulos K., Viteri G., Forner O., Marin-Garcia P., Arnau V., et al. Reactome pathway analysis: a high-performance in-memory approach. BMC Bioinf. 2017;18(1):142. doi: 10.1186/s12859-017-1559-2. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib71] 71.Fabregat A., Sidiropoulos K., Viteri G., Marin-Garcia P., Ping P., Stein L., et al. Reactome diagram viewer: data structures and strategies to boost performance. Bioinformatics. 2018;34(7):1208–1214. doi: 10.1093/bioinformatics/btx752. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib72] 72.Fabregat A., Korninger F., Viteri G., Sidiropoulos K., Marin-Garcia P., Ping P., et al. Reactome graph database: efficient access to complex pathway data. PLoS Comput. Biol. 2018;14(1) doi: 10.1371/journal.pcbi.1005968. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib73] 73.Wu G., Haw R. Functional interaction network construction and analysis for disease discovery. Methods Mol. Biol. 2017;1558:235–253. doi: 10.1007/978-1-4939-6783-4_11. [DOI] [PubMed] [Google Scholar]

[bib74] 74.Kyriacou C., Kim S., Bobdiwala S., Bennett P., Bourne T., Terzidou V. The assessment of microRNA expression in pregnancies classified as a pregnancy of unknown location using transvaginal ultrasonography. Ultrasound Obstet. Gynecol. 2019;54(Suppl. 1):1. [Google Scholar]

[bib75] 75.Kufuor R., Kyriacou C., Kim S., Bobdiwala S., Fourie H., Bennett P., et al. The assessment of circulating biomarker expression in pregnancy of unknown location and ectopic pregnancy: a pilot study. Ultrasound Obstet. Gynecol. 2020;56(Suppl. 1):134–135. [Google Scholar]

[bib76] 76.Sullivan-Pyke C., Sansone S., Jou J., Koelper N., Stentz N., Takacs P. Small non-coding RNA as a serum biomarker for non-viable and ectopic pregnancy. Fertil. Steril. 2018;110 [Google Scholar]

[bib77] 77.Zhang S., Sun Q., Jiang X., Gao F. Clinical significance of expression of hsa-mir-1247 and hsa-mir-1269a in ectopic pregnancy due to salpingitis. Exp. Ther. Med. 2018;15(6):4901–4905. doi: 10.3892/etm.2018.5998. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib78] 78.Balaguer N., Moreno I., Herrero M., Gonzáléz-Monfort M., Vilella F., Simón C. MicroRNA-30d deficiency during preconception affects endometrial receptivity by decreasing implantation rates and impairing fetal growth. Am. J. Obstet. Gynecol. 2019;221(1):46.e1–46.e16. doi: 10.1016/j.ajog.2019.02.047. [DOI] [PubMed] [Google Scholar]

[bib79] 79.Kottawatta K.S., So K.H., Kodithuwakku S.P., Ng E.H., Yeung W.S., Lee K.F. MicroRNA-212 regulates the expression of olfactomedin 1 and C-terminal binding protein 1 in human endometrial epithelial cells to enhance spheroid attachment in vitro. Biol. Reprod. 2015;93(5):109. doi: 10.1095/biolreprod.115.131334. [DOI] [PubMed] [Google Scholar]

[bib80] 80.Abu-Halima M., Khaizaran Z.A., Ayesh B.M., Fischer U., Khaizaran S.A., Al-Battah F., et al. MicroRNAs in combined spent culture media and sperm are associated with embryo quality and pregnancy outcome. Fertil. Steril. 2020;113(5) doi: 10.1016/j.fertnstert.2019.12.028. 970–980.e2. [DOI] [PubMed] [Google Scholar]

[bib81] 81.Fang F., Li Z., Yu J., Long Y., Zhao Q., Ding X., et al. MicroRNAs secreted by human embryos could be potential biomarkers for clinical outcomes of assisted reproductive technology. J. Adv. Res. 2021;31:25–34. doi: 10.1016/j.jare.2021.01.003. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib82] 82.Feng F., Lei L., Liao J., Huang X., Shao Y. Circ_0060731 mediated miR-21-5p-PDCD4/ESR1 pathway to induce apoptosis of placental trophoblasts in intrahepatic cholestasis of pregnancy. Tissue Cell. 2022;76 doi: 10.1016/j.tice.2022.101771. [DOI] [PubMed] [Google Scholar]

[bib83] 83.Hromadnikova I., Kotlabova K., Dvorakova L., Krofta L., Sirc J. Postnatal expression profile of microRNAs associated with cardiovascular and cerebrovascular diseases in children at the age of 3 to 11 years in relation to previous occurrence of pregnancy-related complications. Int. J. Mol. Sci. 2019;20(3) doi: 10.3390/ijms20030654. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib84] 84.Hromadnikova I., Kotlabova K., Dvorakova L., Krofta L. Diabetes mellitus and cardiovascular risk assessment in mothers with a history of gestational diabetes mellitus based on postpartal expression profile of microRNAs associated with diabetes mellitus and cardiovascular and cerebrovascular diseases. Int. J. Mol. Sci. 2020;21(7) doi: 10.3390/ijms21072437. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib85] 85.Hromadnikova I., Kotlabova K., Krofta L. A history of preterm delivery Is associated with aberrant postpartal microRNA expression profiles in mothers with an absence of other pregnancy-related complications. Int. J. Mol. Sci. 2021;22(8) doi: 10.3390/ijms22084033. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib86] 86.Hromadnikova I., Kotlabova K., Krofta L. First trimester prediction of preterm delivery in the absence of other pregnancy-related complications using cardiovascular-disease associated microRNA biomarkers. Int. J. Mol. Sci. 2022;23(7) doi: 10.3390/ijms23073951. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib87] 87.Hua R., Zhang X., Li W., Lian W., Liu Q., Gao D., et al. Ssc-miR-21-5p regulates endometrial epithelial cell proliferation, apoptosis and migration via the PDCD4/AKT pathway. J. Cell Sci. 2020;133(23) doi: 10.1242/jcs.248898. [DOI] [PubMed] [Google Scholar]

[bib88] 88.Karakaya C., Guzeloglu-Kayisli O., Uyar A., Kallen A.N., Babayev E., Bozkurt N., et al. Poor ovarian response in women undergoing in vitro fertilization is associated with altered microRNA expression in cumulus cells. Fertil. Steril. 2015;103(6):1469–1476. doi: 10.1016/j.fertnstert.2015.02.035. e1-3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib89] 89.Khan H.L., Bhatti S., Abbas S., Kaloglu C., Isa A.M., Younas H., et al. Extracellular microRNAs: key players to explore the outcomes of in vitro fertilization. Reprod. Biol. Endocrinol. 2021;19(1):72. doi: 10.1186/s12958-021-00754-9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib90] 90.Kim K.H., Kim E.Y., Kim G.J., Ko J.J., Cha K.Y., Koong M.K., et al. Human placenta-derived mesenchymal stem cells stimulate ovarian function via miR-145 and bone morphogenetic protein signaling in aged rats. Stem Cell Res. Ther. 2020;11(1):472. doi: 10.1186/s13287-020-01988-x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib91] 91.Kochhar P., Dwarkanath P., Ravikumar G., Thomas A., Crasta J., Thomas T., et al. Placental expression of miR-21-5p, miR-210-3p and miR-141-3p: relation to human fetoplacental growth. Eur. J. Clin. Nutr. 2022;76(5):730–738. doi: 10.1038/s41430-021-01017-x. [DOI] [PubMed] [Google Scholar]

[bib92] 92.Kolkova Z., Holubekova V., Grendar M., Nachajova M., Zubor P., Pribulova T., et al. Association of circulating miRNA expression with preeclampsia, its onset, and severity. Diagnostics. 2021;11(3) doi: 10.3390/diagnostics11030476. [DOI] [PMC free article] [PubMed] [Google Scholar]

[bib93] 93.Peñaloza E., Soto-Carrasco G., Krause B.J. MiR-21-5p directly contributes to regulating eNOS expression in human artery endothelial cells under normoxia and hypoxia. Biochem. Pharmacol. 2020;182 doi: 10.1016/j.bcp.2020.114288. [DOI] [PubMed] [Google Scholar]

[bib94] 94.Sadri M., Shu J., Kachman S.D., Cui J., Zempleni J. Milk exosomes and miRNA cross the placenta and promote embryo survival in mice. Reproduction. 2020;160(4):501–509. doi: 10.1530/REP-19-0521. [DOI] [PubMed] [Google Scholar]

[bib95] 95.Fan X., Lou J., Zheng X., Wang Y., Wang J., Luo M., et al. Interference with lncRNA NEAT1 promotes the proliferation, migration, and invasion of trophoblasts by upregulating miR-411-5p and inhibiting PTEN expression. Immunopharmacol. Immunotoxicol. 2021;43(3):334–342. doi: 10.1080/08923973.2021.1910834. [DOI] [PubMed] [Google Scholar]

[bib96] 96.Pritchard N., Kaitu'u-Lino T., Harris L., Tong S., Hannan N. Nanoparticles in pregnancy: the next frontier in reproductive therapeutics. Hum. Reprod. Update. 2021;27(2):280–304. doi: 10.1093/humupd/dmaa049. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Maternal plasma microRNAs as potential biomarkers for triaging pregnancies of unknown location and ectopic pregnancy diagnosis

Christopher Kyriacou

Sung Hye Kim

Maria Arianoglou

Shabnam Bobdiwala

Margaret Pikovsky

Nina Parker

Jennifer Barcroft

Maya Al-Memar

Phillip R Bennett

David A MacIntyre

Tom Bourne

Vasso Terzidou

Abstract

Background

Methods

Results

Conclusion

1. Introduction

2. Methods

2.1. Sex as a biological variable

2.2. Recruitment and sample collection

2.3. Study approval

2.4. Data availability

2.5. RNA extraction

2.6. nCounter microRNA assay and data analysis

2.7. Reverse transcription, real-time quantitative polymerase chain reaction (RT-qPCR) and data analysis

2.8. Statistics

3. Results

3.1. Clinical outcome and patient characteristics

Fig. 1.

Table 1.

Table 2.

3.2. Plasma miRNA expression profiling using nCounter assay

Table 3.

Table 4.

Table 5.

Fig. 2.

Fig. 3.

Fig. 4.

3.3. Technical validation of nCounter microRNA profiling using RT-qPCR

Table 6.

Table 7.

Table 8.

3.4. Biological validation of microRNA candidates using RT-qPCR

Table 9.

Fig. 5.

Table 10.

3.5. Assessment of the predictive ability and diagnostic test performance of identified miRNA markers

Fig. 6.

Fig. 7.

3.6. Target gene and pathway enrichment analysis

4. Discussion

CRediT authorship contribution statement

Trial registration

Funding

Declaration of competing interest

Acknowledgements and Funding

Footnotes

Appendix A. Supplementary data

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases